Subtype-specific health and economic impact of delayed breast cancer diagnosis during the early COVID-19 pandemic in Belgium: A Markov model analysis

Subtype-specific health and economic impact of delayed breast cancer diagnosis during the early COVID-19 pandemic in Belgium: A Markov model analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Subtype-specific health and economic impact of delayed breast cancer diagnosis during the early COVID-19 pandemic in Belgium: A Markov model analysis Yasmine Khan, Nick Verhaeghe, Chris Monten, Katrien Vanthomme, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7786399/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Dec, 2025 Read the published version in Breast Cancer Research → Version 1 posted 9 You are reading this latest preprint version Abstract Background: During the first COVID-19 wave, breast cancer diagnoses declined sharply worldwide due to suspended screening programmes and delayed care-seeking driven by fear of infection. In Belgium, the national programme was halted from March to June 2020, leaving 135 invasive breast cancers undiagnosed. Although no stage shifts were observed in 2020, these undiagnosed cases risk later detection at more advanced stages, with worse prognosis, higher healthcare costs, and reduced health-related quality of life. Evidence indicates that such delays disproportionately affect aggressive subtypes (e.g., triple-negative (TNBC)) compared with slower-growing luminal-like cancers. This study projected the five-year impact of these diagnostic delays on health outcomes and costs, stratified by molecular subtype. Methods: A Markov cohort model compared two cohorts of 10,147 Belgian women with breast cancer: a “disrupted-care” cohort (2020 data, including 135 undiagnosed cases) and a “non-disrupted” cohort (2017–2019 trends). Outcomes over five years were estimated from the healthcare payer perspective, including incremental QALYs, direct medical costs, and mortality. Data sources included the Belgian Cancer Registry, literature, and national cost databases. Sensitivity and scenario analyses assessed uncertainty. Results: Over five years, the diagnostic delays were projected to cause a total loss of 21 QALYs and €3.2M in additional healthcare costs across all subtypes, resulting in an estimated six additional deaths. This corresponds to a modest average impact of 0.002 QALYs and €315 per patient. The burden was disproportionately carried by aggressive subtypes. TNBC accounted for the largest health loss (-9.5 QALYs) and highest incremental costs (€1.6M), followed by HER2+ cancer (-2.5 QALYs; €0.5M). Probabilistic sensitivity analysis revealed considerable uncertainty in these estimates, particularly influenced by assumed input parameters. Conclusion: The impact of diagnostic delays during Belgium’s first COVID-19 wave was less severe than expected, likely because rapid recovery measures prevented a sustained stage shift. However, the overall modest results may mask a greater burden among faster-progressing subtypes such as TNBC and HER2+. The high uncertainty in the model underscores the need for better subtype-specific data. Ensuring diagnostic continuity, particularly for high-risk cancers, will be essential to mitigate the impact of future health system disruptions. Breast cancer COVID-19 Diagnostic delay Molecular subtypes Markov model Health economics QALY Belgium Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 | BACKGROUND Cancer remains a leading cause of morbidity and mortality worldwide, placing a substantial health and economic burden on societies. In Europe alone, total cancer-related costs were estimated at €199 billion in 2018( 1 – 3 ), including direct medical expenses, indirect costs (e.g., productivity losses), and direct non-medical costs like informal caregiving. Breast cancer is the most prevalent cancer among women and the second costliest cancer globally( 4 , 5 ). Belgium has among the highest age-standardized breast cancer incidence rates in the world( 6 ), contributing substantially to the national healthcare burden. In 2018, breast cancer healthcare costs exceeded €300 million( 4 ), driven by its high incidence—11,636 new cases in 2023( 7 )—and an aging population, which increases disease risk and leads to more complex care ( 4 ). Despite the high costs, breast cancer has a relatively low burden of years lived with disability per case( 4 ). This is largely due to early detection—often through organized screening programs—and effective treatment, which improve prognosis and reduce recurrence and long-term disability( 4 ). These outcomes are further supported by Belgium’s structured breast cancer care pathway, with accredited clinics, mandatory multidisciplinary consultations, and national standards for timely, coordinated, high-quality care( 8 ). During the first wave of COVID-19, many countries implemented strict public health measures, including lockdowns, social distancing, and healthcare resource reallocation, to contain the spread of the virus( 9 ). These led to suspension of non-urgent services and disrupted breast cancer care globally, with halted screening and adapted treatments to reduce exposure and preserve capacity( 10 ). There was also concern that patients delayed seeking care due to fear of infection, healthcare strain, or a desire not to burden the system( 11 , 12 ). Delayed breast cancer care could theoretically lead to more advanced-stage presentations, resulting in higher treatment costs, reduced health-related quality of life (HRQoL), and poorer survival( 13 , 14 ). In Belgium, the organized breast cancer screening programme was suspended from March to June 2020, causing a 44% drop in Ductal Carcinoma In Situ (DCIS) diagnoses( 15 ). As DCIS is mainly detected through screening, this decline reflects programme disruption, though some were still found symptomatically. By year-end, 5% of breast cancer cases—both DCIS and invasive—remained undiagnosed despite screening resumption¹¹. Importantly, Belgian data show that time to treatment was largely maintained or even accelerated, with no increase in tumour size or nodal involvement among symptomatic cases( 15 ). Breast cancer is a heterogenous disease with multiple subtypes that differ in biological behaviour and clinical outcomes. The most widely used classification system relies on immunohistochemical profiling of estrogen, progesterone, and human epidermal growth factor 2 (HER2) receptors. Based on these markers, breast cancers are commonly grouped into four subtypes (Table 1 ): luminal A, luminal B, HER2-positive (HER2+), and triple-negative (TNBC). This classification guides treatment decisions and reflects differences in prognosis and response to therapy( 16 , 17 ). Luminal A cancers tend to grow slowly and have a favourable prognosis, whereas HER2 + and TNBC subtypes are more aggressive, with faster progression and worse outcomes if diagnosis is delayed( 12 , 18 ). In Belgium, data from 2014 show that luminal A-like tumours accounted for 54% of cases, followed by luminal B-like (27%), TNBC (13%), and HER2+ (6%) breast cancers( 19 ). The impact of a temporary disruption of cancer screening on HRQoL, healthcare costs, and mortality across breast cancer molecular subtypes in Belgium remains unknown. However, evidence from other countries indicates that even short-term screening interruptions may result in more advanced-stage diagnoses( 20 ), higher projected mortality( 21 – 24 )—particularly among aggressive subtypes—and substantial long-term HRQoL and economic burdens( 24 , 25 ). A recent study( 26 ) found that even short diagnostic delays can lead to more advanced cancer, especially for aggressive subtypes. For TNBC, a delay of 38 days caused more than 10% of patients to be diagnosed at a later stage. This window was 52 days for HER2 + cancers but a longer 85 days for slower-growing luminal-like cancers, highlighting the heightened sensitivity of aggressive tumours to diagnostic delays. Although Belgian data did not indicate tumour or nodal progression among breast cancer cases with delayed diagnosis between March and June 2020, these analyses were not stratified by molecular subtype( 15 ). As such, potential stage progression in aggressive subtypes, such as TNBC or HER2 + cancers, may have been masked by the predominance of slower-growing tumours like luminal A. Through a Markov cohort simulation model, we explore the potential hidden impact of these diagnostic delays on changes in Quality-Adjusted Life Years (QALYs) and healthcare costs, across molecular subtype, compared to pre‑COVID‑19 care conditions. In line with Belgian guidelines for health economic evaluations( 27 ), these standard metrics were selected to capture the comprehensive patient health burden and the direct economic impact on the healthcare system, respectively. We projected the impact over a five-year horizon, as this is the clinically critical period when the risk of cancer recurrence is highest and the negative health and economic consequences of a delayed diagnosis are most likely to emerge( 28 ). This timeframe is also supported by the most robust clinical evidence on treatment outcomes and costs( 16 , 17 ). These findings aim to provide healthcare professionals and policymakers with evidence to safeguard continuity of diagnostic services during future system shocks, minimise backlogs once services resume, prioritise timely detection of aggressive subtypes such as TNBC and HER2 + cancers, and encourage further breast cancer research stratified by molecular subtype. 2 | METHODS We used a Markov cohort simulation model approach, as described in our protocol (29), to project over five years the health and healthcare cost impacts of COVID-19-attributable breast cancer diagnostic disruptions that occurred from mid-March to June 2020 in Belgium. Markov models reflect well breast cancer’s chronic nature, stage progression, and long-term recurrence and mortality risk(30). The analysis was stratified by four breast cancer molecular subtypes (Table 1): luminal A, luminal B, HER2+, and TNBC. Table 1 Characteristics of the four breast cancer molecular subtypes (18) Molecular subtype Receptor status Biological behaviour Prognosis Treatment Luminal A ER+, PR+, HER2–, low Ki-67 Slow-growing, hormone-sensitive Favourable Endocrine therapy Luminal B ER+, PR±, HER2– or HER2+, high Ki-67 More proliferative than Luminal A Intermediate Endocrine therapy + CDK4/6 inhibitors, ± chemotherapy HER2+ HER2+, ER–/low, PR–/low Highly proliferative, treatment-sensitive Variable (improved with HER2-targeted therapy) Anti-HER2 therapy (e.g., trastuzumab, pertuzumab) + chemotherapy TNBC ER–, PR–, HER2– Highly aggressive, rapid progression Poor Chemotherapy ± immunotherapy ER: Estrogen receptor; PR: Progesterone receptor; HER2: Human Epidermal Growth Factor Receptor 2; TNBC: Triple-negative breast cancer. *Ki-67 = a marker of cellular proliferation, used to distinguish Luminal A (low Ki-67) from Luminal B (high Ki-67) **CDK4/6 = cyclin-dependent kinases 4 and 6, targeted by specific inhibitors in HR+/HER2 − breast cancer to block cell cycle progression. 2.1 | Study design Aggregated, de-identified incidence data by clinical stage were obtained from the Belgian Cancer Registry (BCR, 2024), which compiles nationwide data from oncology programmes and pathology laboratories (age, stage, tumour and patient characteristics)(31) and exempted this study from ethical review. COVID-19–related diagnostic disruption was represented by 135 undiagnosed cancer (UDC) patients that should have been diagnosed in 2020 based on 2017–2019 predictions. Two subtype-specific Markov cohort models were developed, each simulating over five years the movement of two closed incident cohorts of 10,147 adult female breast cancer patients: a non-disrupted care cohort, estimated from 2017–2019 trends using a Poisson model, and a disrupted-care cohort reflecting observed 2020 cases (symptomatic, opportunistic, and resumed screening). Predicted stage-specific values were adjusted to match the observed decline, ensuring equal cohort sizes for unbiased comparison. UDC were derived by subtracting observed stage I–IV cases in 2020 from the corrected predictions. Age stratification was not applied, as molecular subtype is the primary driver of prognosis and treatment decisions(16, 17). While age may influence care through comorbidities or patient preference, including it would have added complexity without improving model validity. Because subtype-specific incidence data were unavailable, pre-COVID Belgian subtype proportions were applied to both cohorts by multiplying each stage count by the corresponding subtype share to estimate per-subtype counts. A five-year time horizon was chosen to capture the period of highest recurrence risk and cost concentration, particularly for aggressive subtypes such as TNBC and HER2+(28), and to align with follow-up guidelines and available evidence. A three-month cycle length was used, reflecting typical treatment intervals (e.g., chemotherapy cycles, clinical follow-ups) (16). The model adopted a Belgian healthcare payer perspective, focusing on direct medical costs (treatments, consultations), as this reflects the National Institute for Health and Disability Insurance (NIHDI) reimbursement practice and avoids uncertainty associated with missing subtype-specific data on productivity losses. The model was developed in Excel, with patients distributed at baseline (cycle 0) across the initial health states—‘Stage I Diagnosis &Treatment (D&T)’, ‘Stage II D&T’, ‘Stage III D&T’, ‘Stage IV D&T’, and ‘UDC’ (see Fig. 1)—according to BCR incidence data. Each cohort then progressed through health states via a transition matrix, which applied predefined probabilities to redistribute patients every three months. Each health state was associated with specific healthcare costs and utilities (see 2.4 Data inputs). By iterating these cycles, the simulation generated a dynamic representation of the patient population’s trajectory over the five-year horizon. Ultimately, the model compared QALYs (where 1 = perfect health and 0 = death) and healthcare costs between cohorts. Input parameters for the simulation, including transition probabilities, healthcare costs, and utilities, are detailed in Table 4, with calculation methods provided in the Additional file 1. Table 2 Breast cancer cases by stage and subtype in non-disrupted (2017–2019) and disrupted (2020) cohorts (aggregated data from the Belgian Cancer Registry, 2024) Non-disrupted care cohort (Pre-COVID-19 situation, 2017–2019) Stage (clinical) at diagnosis Total Luminal A Luminal B HER2-positive Triple-negative Stage I 4 769 (47.00%) 2 575 1 288 286 620 Stage II 3 904 (38.47%) 2 108 1 054 234 508 Stage III 716 (7.06%) 387 193 43 93 Stage IV 758 (7.47%) 409 205 45 99 Total 10 147 5 479 2 740 609 1 319 Disrupted-care cohort (COVID-19 situation, 2020) Stage (clinical) at diagnosis Total Luminal A Luminal B HER2-positive Triple-negative Stage I 4 551 (44.85%) 2 458 1 229 273 592 Stage II 3 949 (38.92%) 2 132 1 066 237 513 Stage III 753 (7.42%) 407 203 45 98 Stage IV 759 (7.48%) 410 205 46 99 Undiagnosed Cancer 135 (1.33%) 73 36 8 18 Total 10 147 5 479 2 740 609 1 319 Note: Values represent aggregated patient counts and have been rounded; minor discrepancies may occur. Table 4 Baseline input parameters for non-disrupted care (pre-COVID-19) and disrupted care (COVID-19) cohorts by molecular subtype Transition probabilities Input parameters Subtype Base Value Standard Error OSA – 95% CI Lower Bound OSA – 95% CI Upper Bound Source(s) Stage I D & T to Dead Luminal A 0.002 0.001 0.001 0.004 (34) Luminal B 0.010 0.002 0.006 0.013 HER2+ 0.016 0.002 0.012 0.021 TNBC 0.017 0.002 0.012 0.02 Stage II D & T to Dead Luminal A 0.005 0.001 0.002 0.007 Luminal B 0.012 0.002 0.008 0.016 HER2+ 0.019 0.003 0.014 0.024 TNBC 0.019 0.003 0.014 0.024 Stage III D & T to Dead Luminal A 0.012 0.002 0.008 0.016 Luminal B 0.020 0.003 0.015 0.025 HER2+ 0.026 0.003 0.020 0.032 TNBC 0.027 0.003 0.021 0.032 Stage I-III FU to Stage I-III LRR Luminal A 0.003 0.001 0.001 0.004 (28) Luminal B 0.005 0.002 0.002 0.008 HER2+ 0.006 0.003 0.000 0.013 TNBC 0.007 0.003 0.002 0.012 Stage I-III FU to Stage IV D & 1L Luminal A 0.005 0.001 0.003 0.007 Luminal B 0.011 0.002 0.007 0.016 HER2+ 0.015 0.005 0.004 0.025 TNBC 0.013 0.004 0.006 0.020 Stage I-III FU to Dead Luminal A 0.001 0.000 0.000 0.001 Luminal B 0.001 0.001 0.000 0.002 HER2+ 0.001 0.002 0.000 0.006 TNBC 0.002 0.001 0.000 0.004 Stage I-III LRR to Dead Luminal A 0.011 0.002 0.008 0.014 (35) Luminal B 0.032 0.005 0.023 0.042 HER2+ 0.020 0.004 0.013 0.028 TNBC 0.035 0.006 0.023 0.046 Stage IV D & 1L to Stage IV 2L Luminal A 0.060 0.005 0.051 0.069 (36) Luminal B 0.060 0.005 0.051 0.069 HER2+ 0.054 0.008 0.039 0.069 TNBC 0.062 0.012 0.038 0.085 Stage IV D & 1L to Dead Luminal A 0.005 0.001 0.003 0.008 Luminal B 0.005 0.001 0.003 0.008 HER2+ 0.007 0.004 0.000 0.014 TNBC 0.012 0.005 0.002 0.023 Stage IV 2L to Stage IV 3L Luminal A 0.073 0.006 0.061 0.085 Luminal B 0.073 0.006 0.061 0.085 HER2+ 0.065 0.010 0.045 0.086 TNBC 0.065 0.014 0.037 0.093 Stage IV 2L to Dead Luminal A 0.008 0.002 0.004 0.012 Luminal B 0.008 0.002 0.004 0.012 HER2+ 0.007 0.004 0.000 0.014 TNBC 0.013 0.007 0.000 0.026 Stage IV 3L to Stage IV 4L Luminal A 0.073 0.007 0.059 0.087 Luminal B 0.073 0.007 0.059 0.087 HER2+ 0.063 0.013 0.039 0.088 TNBC 0.057 0.016 0.026 0.087 Stage IV 3L to Dead Luminal A 0.009 0.003 0.004 0.014 Luminal B 0.009 0.003 0.004 0.014 HER2+ 0.009 0.005 0.000 0.018 TNBC 0.018 0.009 0.000 0.035 Stage IV 4L to Dead Luminal A 0.010 0.003 0.004 0.015 Luminal B 0.010 0.003 0.004 0.015 HER2+ 0.010 0.010 0.000 0.031 TNBC 0.022 0.010 0.003 0.041 UDC to Stage I D & T Luminal A 0.400 0.020 0.361 0.439 Hypothesized based on clinical evidence of stage progression after diagnostic delay (26) Luminal B 0.300 0.020 0.261 0.389 HER2+ 0.100 0.020 0.061 0.139 TNBC 0.050 0.020 0.011 0.089 UDC to Stage II D & T Luminal A 0.400 0.020 0.361 0.439 Luminal B 0.350 0.020 0.311 0.389 HER2+ 0.150 0.020 0.111 0.189 TNBC 0.050 0.020 0.011 0.089 UDC to Stage III D & T Luminal A 0.100 0.020 0.061 0.139 Luminal B 0.150 0.020 0.111 0.189 HER2+ 0.300 0.020 0.261 0.339 TNBC 0.100 0.020 0.061 0.139 UDC to Stage IV D & 1L Luminal A 0.080 0.020 0.041 0.119 Luminal B 0.150 0.020 0.111 0.189 HER2+ 0.350 0.020 0.311 0.389 TNBC 0.750 0.020 0.711 0.789 UDC to Dead Luminal A 0.010 0.020 0.000 0.049 Luminal B 0.025 0.020 0.000 0.064 HER2+ 0.035 0.020 0.000 0.074 TNBC 0.040 0.020 0.001 0.079 Utilities Input parameters Subtype Base Value Standard Error Disutility value OSA – 95% CI Lower Bound OSA – 95% CI Upper Bound Source(s) UDC Luminal A 0.82 0.005 - 0.811 0.829 (37) Luminal B 0.82 0.005 0.811 0.829 HER2+ 0.82 0.005 0.811 0.829 TNBC 0.82 0.005 0.811 0.829 Stage I D & T Luminal A 0.787 0.022 0.033 0.744 0.829 (38) Luminal B 0.787 0.022 0.033 0.744 0.829 HER2+ 0.770 0.077 0.050 0.620 0.920 (39–41) TNBC 0.755 0.077 0.065 0.605 0.905 (41, 42) Stage II D & T Luminal A 0.787 0.022 0.033 0.744 0.829 (38) Luminal B 0.787 0.022 0.033 0.744 0.829 HER2+ 0.770 0.077 0.050 0.620 0.920 (39–41) TNBC 0.755 0.077 0.065 0.605 0.905 (41, 42) Stage III D &T Luminal A 0.782 0.025 0.038 0.733 0.831 (38) Luminal B 0.782 0.025 0.038 0.733 0.831 HER2+ 0.760 0.077 0.060 0.610 0.910 (39–41) TNBC 0.745 0.077 0.075 0.95 0.895 (41, 42) Stage I-III FU (adjuvant therapy) Luminal A 0.772 0.021 0.048 0.731 0.812 (38) Luminal B 0.772 0.021 0.048 0.731 0.812 HER2+ 0.733 0.041 0.087 0.654 0.813 TNBC 0.733 0.041 0.087 0.654 0.813 Stage I-III FU (post-adjuvant therapy) Luminal A 0.814 0.005 0.006 0.805 0.823 (38) Luminal B 0.814 0.005 0.006 0.805 0.823 HER2+ 0.814 0.005 0.006 0.805 0.823 TNBC 0.814 0.005 0.006 0.805 0.823 Stage I-III LRR Luminal A 0.770 0.007 0.050 0.757 0.783 (43) Luminal B 0.770 0.007 0.050 0.757 0.783 HER2+ 0.730 0.077 0.090 0.580 0.880 TNBC 0.710 0.077 0.110 0.560 0.860 Stage IV D & T Luminal A 0.730 0.009 0.090 0.712 0.748 (43) Luminal B 0.730 0.009 0.090 0.712 0.748 HER2+ 0.690 0.077 0.130 0.540 0.840 (39) TNBC 0.670 0.077 0.150 0.520 0.820 (44) Stage IV 2L Luminal A 0.720 0.077 0.100 0.570 0.870 (45) Luminal B 0.720 0.016 0.100 0.570 0.870 HER2+ 0.680 0.077 0.140 0.530 0.830 (46) TNBC 0.650 0.077 0.170 0.500 0.800 (47) Stage IV 3L Luminal A 0.700 0.077 0.120 0.550 0.850 (46) Luminal B 0.700 0.077 0.120 0.550 0.850 HER2+ 0.680 0.077 0.140 0.530 0.830 (48) TNBC 0.630 0.077 0.190 0.480 0.780 (49) Stage IV 4L Luminal A 0.690 0.077 0.130 0.540 0.840 (50, 51) Luminal B 0.690 0.077 0.130 0.540 0.840 HER2+ 0.660 0.077 0.160 0.510 0.810 (52) TNBC 0.600 0.077 0.220 0.450 0.750 (53) Direct healthcare costs Input parameters Subtype Base Value Standard Error OSA – 95% CI Lower Bound OSA – 95% CI Upper Bound Source(s) Stage I D & T Luminal A 4 548 455 3 657 5 440 NomenSoft nomenclature codes (see the Additional file 1) Luminal B 4 548 455 3 657 5 440 HER2+ 7 563 756 6 081 9 046 TNBC 28 352 2 835 22 795 33 910 Stage II D & T Luminal A 4 548 455 3 657 5 440 Luminal B 4 548 455 3 657 5 440 HER2+ 12 601 1 260 10 131 15 071 TNBC 28 352 2 835 22 795 33 910 Stage III D &T Luminal A 5 523 552 4 441 6 606 Luminal B 5 523 552 4 441 6 606 HER2+ 13 576 1 358 10 915 16 236 TNBC 29 327 2 933 23 579 35 075 Stage I-III FU (adjuvant therapy) Luminal A 536 54 431 641 BCPI(54) Luminal B 536 54 431 641 HER2+ 16 105 1 610 12 948 19 262 TNBC 23 804 2 380 19 139 28 470 Stage I-III FU (post-adjuvant therapy) Luminal A 334 33 269 400 NomenSoft nomenclature codes (see the Additional file 1) Luminal B 334 33 269 400 HER2+ 334 33 269 400 TNBC 334 33 269 400 Stage I-III LRR Luminal A 4 873 487 3 918 5 828 Mean Stage I-III D&T costs Luminal B 4 873 487 3 918 5 828 HER2+ 11 247 1 125 9 042 13 451 TNBC 28 677 2,868 23 057 34 298 Stage IV D & T Luminal A 5 934 593 4 771 7 098 BCPI(54) Luminal B 5 934 593 4 771 7 098 HER2+ 16 385 1 638 13 173 19 596 TNBC 24 649 2 465 19 818 29 480 Stage IV 2L Luminal A 5 922 592 4 762 7 083 Luminal B 5 922 592 4 762 7 083 HER2+ 14 537 1 454 11 688 17 386 TNBC 25 038 2 504 20 131 29 945 Stage IV 3L Luminal A 28 976 2 898 23 297 34 655 Luminal B 28 976 2 898 23 297 34 655 HER2+ 4 768 477 3 833 5 702 TNBC 9 374 937 7 537 11 211 Stage IV 4L Luminal A 1 261 126 1 014 1 509 Luminal B 1 261 126 1 014 1 509 HER2+ 19 663 1 966 15 809 23 518 TNBC 1 245 124 1 001 1 488 Scenario Analysis Input parameters Subtype Base Value Standard Error Scenario Analysis - Multiplier OSA – 95% CI Lower Bound OSA – 95% CI Upper Bound Source(s) UDC to Stage I D & T Luminal A 0.10 0.001 0.25 0.098 0.102 Hypothesized based on clinical evidence of stage progression after diagnostic delay (26) Luminal B 0.09 0.001 0.30 0.088 0.092 HER2+ 0.05 0.001 0.50 0.049 0.051 TNBC 0.01 0.000 0.20 0.009 0.011 UDC to Stage II D & T Luminal A 0.10 0.001 0.25 0.098 0.102 Luminal B 0.12 0.001 0.35 0.120 0.125 HER2+ 0.05 0.001 0.35 0.051 0.054 TNBC 0.01 0.000 0.20 0.009 0.011 UDC to Stage III D & T Luminal A 0.25 0.001 2.50 0.247 0.253 Luminal B 0.30 0.001 2.00 0.297 0.303 HER2+ 0.35 0.002 1.15 0.342 0.348 TNBC 0.05 0.001 0.50 0.039 0.041 UDC to Stage IV D & 1L Luminal A 0.52 0.002 6.50 0.517 0.523 Luminal B 0.45 0.002 3.00 0.447 0.453 HER2+ 0.46 0.002 1.30 0.452 0.458 TNBC 0.86 0.001 1.15 0.860 0.865 UDC to Dead Luminal A 0.01 0.000 1.00 0.009 0.011 Luminal B 0.03 0.000 1.00 0.024 0.026 HER2+ 0.04 0.001 1.00 0.034 0.036 TNBC 0.04 0.001 1.00 0.039 0.041 D: Diagnosis; FU: Follow-Up; LRR: Locoregional Recurrence; OSA: One-way Sensitivity Analysis; T: Treatment; TNBC: Triple-Negative Breast Cancer; UDC: Undiagnosed Cancer; 1L: 1st systemic therapy line; 2L: 2nd systemic therapy line; 3L: 3rd systemic therapy line; 4L: 4th systemic therapy line * Bold input parameters represent those only present during the disrupted care situation (COVID-19) ** 95% CI: 95% confidence interval, which was calculated as follows: Base case value ± 1.96 x SE *** Standard errors were calculated using \(\:SE=SD/\sqrt{n}\) for means and \(\:SE=/\sqrt{p(1-p)/n}\) for proportions **** Hypothesized input values were underlined 2.2 | Markov Model structure The model consisted of 11 health states (Fig. 1), grouped into non-metastatic (stages I–III) and metastatic (stage IV) disease. Non-metastatic disease begins with a 'diagnosis and treatment' (D&T) phase, which includes diagnostic work-up, neoadjuvant therapy (for HER2 + and TNBC), surgery (with reconstruction if applicable), and radiotherapy. This phase is represented in the model as 'Stage I D&T,' 'Stage II D&T,' or 'Stage III D&T,' depending on the initial stage at diagnosis. Patients remain in the D&T state for one or more cycles depending on their subtype-specific treatment duration (Table 3), before transitioning to 'Stage I–III Follow-Up (FU)' for a defined period of adjuvant therapy (AT). We modelled AT as five years (20 cycles) for luminal A and seven years (28 cycles) for luminal B, reflecting standard practice for hormone receptor–positive disease and extended therapy increasingly recommended for luminal B due to its higher risk of late recurrence(32). Given the differing nature and duration of AT across molecular subtypes, a distinct ‘Stage I–III FU’ health state was defined. After AT, patients enter this state with follow-up every three months until recurrence. Some, however, may not achieve a disease-free period and progress directly to recurrence. In both cases, we simplify this by saying that patients move to ‘Stage I–III Locoregional Recurrence (LRR)’ if the evolution is locoregional or to ‘Stage IV D&T’ if metastatic. Those in ‘Stage I–III LRR’ receive treatment during one cycle and then return to follow-up, while patients entering the metastatic pathway (Stage IV states) start systemic therapy in ‘Stage IV D&T’ and transition through subsequent lines of treatment as disease advances. Table 3 Duration spent in initial treatment-related health states by molecular subtype, independent of COVID-19 disruptions. Health States Luminal A Luminal B HER2+ Triple-Negative Stage I D & T 3 months (1 cycle) 3 months (1 cycle) 9 months (3 cycles) 9 months (3 cycles) Stage II D & T 3 months (1 cycle) 3 months (1 cycle) 9 months (3 cycles) 9 months (3 cycles) Stage III D & T 3 months (1 cycle) 3 months (1 cycle) 9 months (3 cycles) 9 months (3 cycles) Stage I-III FU (Adjuvant therapy) 5 years (20 cycles) 7 years (28 cycles) 1 year (4 cycles) 1 year (4 cycles) D&T: diagnosis and treatment; FU: follow-up Note: Stage IV (metastatic) disease is not included in this table, as it follows a distinct clinical pathway characterized by successive lines of systemic therapy rather than structured, stage-based treatment and follow-up. The durations listed here reflect initial, curative-intent treatment for non-metastatic disease (stages I–III) prior to progression. Patients diagnosed de novo with metastatic disease also start at ‘Stage IV D&T’, where they receive a first line of systemic therapy. They remain in this state (illustrated through the looping arrow) until the previous treatment lose effectiveness, or in case of progression (when the disease never stabilized), directly transition to the second-line regimen. This process continues sequentially up to four lines of metastatic treatment: ‘Stage IV 2L’, ‘3L’, and ‘4L’. A separate health state, ‘Undiagnosed Cancer’ (UDC), captures patients whose diagnosis was delayed due to COVID-19-related disruptions and who can subsequently transition to any stage at diagnosis once detected. During any cycle, patients in any health state can transition to ‘Dead’ (absorbing state). D & T: Diagnosis and treatment; FU: Follow-up; L: Lines of systemic therapy; LRR: Locoregional recurrence L: Lines of systemic therapy; UDC : Undiagnosed cancer ↺ = Probability of remaining in the state → = Probability of moving to another state ] = Probability for patients in any state to move to the dead state 2.3 | Model validation Clinical validation was conducted through consultation rounds with oncologists C.M. and E.N., to validate the model structure (i.e., model diagram), input parameters (i.e., healthcare costs), and key assumptions (i.e., simplified treatment pathways), ensuring alignment with current clinical practice and expert opinion. Additionally, five‑ and ten‑year mortality outcomes from the non-disrupted care model were validated against published subtype‑specific survival rates(33), converted to mortality values (mortality = 1 – survival), which were consistent with our estimates. 2.4 | Data inputs To operationalise our Markov model, aggregated incidence data from the BCR were used, transition probabilities and utilities were extracted from the literature, and healthcare costs were sourced from the Belgian medical nomenclature database (NomenSoft) and the Belgian pharmacotherapeutic information database (BCPI). All input values were stratified by health state and molecular subtype. The selected input values are presented in Table 4, with calculation methods and rationale detailed in the Additional file 1. Both models used the same input values, except for aggregated incidence data and transition probabilities from the UDC state to stage I–IV D&T. In the non-disrupted care model, UDC cases were absent and no such transitions occurred, as no care disruptions took place. In contrast, the disrupted care model included UDC cases with hypothesized transition probabilities to stage I–IV D&T to reflect diagnostic delays. Insert Table 4 here 2.4.1 | Epidemiological data As mentioned above, stage-specific incidence data for breast cancer diagnoses for the disrupted and non-disrupted care cohorts were obtained from the BCR. Under 5% of diagnoses in the BCR dataset lacked definitive staging (unknown stage). Reasons for incomplete staging differed between non-disrupted care cohort (e.g., frail elderly) and disrupted care cohort (e.g., potentially, interrupted diagnostics due to intensive care unit admission). Given the uncertainty in distribution, the minimal overall percentage, and the absence of a statistically significant difference between cohorts, unknown stage cases were excluded from the main model to ensure consistent comparisons. 2.4.2 | Transition probabilities The 3-month subtype-specific transition probabilities were mainly derived from international retrospective cohort studies (Table 4). We hypothesized 3-month transition probabilities from UDC to ‘Stage I–IV D&T’ and ‘Dead’ for each molecular subtypes—given the absence of subtype-specific incidence data on UDC—based on a study which showed that aggressive subtypes (i.e., TNBC and HER2+) reach stage progression much earlier than luminal-like cancers (26). 2.4.5 | Healthcare costs Healthcare costs for each health state were estimated from the perspective of the Belgian healthcare payer (NIHDI). The model's costs represent the official reimbursed amounts (nomenclature codes 1300/1600), which excludes patient co-payments (codes 3300/3600) to reflect only the payer's expenditure. Diagnostic, surgical, reconstruction, and radiotherapy costs were retrieved from NomenSoft(55), which provides official reimbursement rates (codes are available in the Additional file 1). Subtype-specific drug costs for neoadjuvant and AT were obtained from the Belgian Centre for Pharmacotherapeutic Information (BCPI)(54), with dosages and schedules based on phase III trials and validated by two oncologists (C.M., E.N.). All reimbursement values were indexed to 2020 prices using the Belgian health index (56) to ensure consistency with the model’s reference year. Subtype-specific treatment pathways (Table 5) were simplified to balance between typical clinical practice and ensure feasibility within the Markov framework, and were based on ESMO clinical practice guidelines(16, 17), which underpin Belgian national recommendations. Some treatment regimens were generalized, and treatment durations were averaged across patient groups. A standardized diagnostic pathway was assumed across all molecular subtypes, including biopsy, staging imaging, blood analysis, and mammography. For simplicity, we assumed only patients with HER2 + and TNBC to have subtype-specific neoadjuvant therapy. Surgery was stratified by stage: All stage I-II patients underwent breast-conserving surgery (BCS) without reconstruction, while for stage III patients, 70% underwent BCS and 30% underwent mastectomy, with 25% assumed to receive reconstruction. All patients received the standard hypofractionated whole-breast radiotherapy regimen of 15 fractions, and related consultations before, during, and after treatment. AT also differed based on the molecular subtype. Post-adjuvant therapy follow-up included annual mammography, ultrasonography, and four consultations per year. For stage I-III patients developing LRR, healthcare costs were assumed to mirror those of initial D&T, calculated as the mean cost across stages I-III. Metastatic disease treatments followed subtype-specific systemic therapy lines, with healthcare costs also estimated on a 3-month cycle basis for comparability across subtypes. For simplicity, luminal B was assumed to have the same costs as luminal A. Table 5 Simplified treatment pathway across health states and molecular subtype Health state Luminal-like HER2+ TNBC Stage I D & T Diagnosis + BCS + RT (+ pre, per, post consultations) Diagnosis + Neoadjuvant therapy + BCS + RT (+ pre, per, post consultations) Diagnosis + Neoadjuvant therapy + BCS + RT (+ pre, per, post consultations) Stage II D & T Diagnosis + BCS + RT (+ pre, per, post consultations) Diagnosis + Neoadjuvant therapy + BCS + RT (+ pre, per, post consultations) Diagnosis + Neoadjuvant therapy + BCS + RT (+ pre, per, post consultations) Stage III D & T Diagnosis + 70% BCS and 30% MST (+ 25% reconstruction) + RT (including pre, per, post consultations) Diagnosis + Neoadjuvant therapy + 70% BCS and 30% MST (+ 25% reconstruction) + RT (including pre, per, post consultations) Diagnosis + Neoadjuvant therapy + 70% BCS and 30% MST (+ 25% reconstruction) + RT (including pre, per, post consultations) Stage I-III FU (Adjuvant therapy) Adjuvant endocrine therapy: Either: tamoxifen/letrozole/ Exemestane/ anastrozole + triptorelin/goserelin (if premenopausal women) + CDK4 inhibitors: Abemaciclib Chemotherapy +Anti-HER2 therapy (+ Endocrine therapy if HER2+) Immunotherapy and Chemotherapy Stage I-III FU (Post-adjuvant therapy) Mammography Ultrasonography 4 consultations Mammography Ultrasonography 4 consultations Mammography Ultrasonography 4 consultations Stage I-III LRR Same as ‘Stage I-III D&T’ Same as ‘Stage I-III D&T’ Same as ‘Stage I-III D&T’ Stage IV D & T Diagnosis + endocrine therapy and CDK4/6 inhibitors Anti-HER2 and Chemotherapy Immunotherapy and Chemotherapy Stage IV 2L Targeted therapy Anti-HER2 Targeted therapy Stage IV 3L Chemotherapy for patients with endocrine resistance Chemotherapy and Anti-HER2 Chemotherapy Stage IV 4L Chemotherapy for patients with endocrine resistance Chemotherapy and Anti-HER2 Chemotherapy BCS: breast-conserving surgery; CDK4/6 inhibitors: cyclin-dependent kinases 4 and 6; D&T: Diagnosis and treatment; FU: Follow-up; HER2: Human epidermal growth factor receptor 2–positive; LRR: Locoregional recurrence; MST: Mastectomy; RT: Radiotherapy 2.4.6 | Utilities Health-state utilities were derived from EQ-5D-5L data(37, 38, 43) using a two-step approach. Baseline utilities for Belgian women of all ages were taken from the national Health Interview Survey (37). Health-state-specific disutilities from international studies (38, 43) were then subtracted, assuming comparable disease impacts across similar countries (e.g., the Netherlands). When disutilities were unavailable, values were estimated based on literature(39–42, 44–49, 51–53, 57–60), considering subtype-specific treatment burden, disease progression, and HRQoL changes. Utilities for luminal B were assumed to be equal to those of luminal A. Utilities for the UDC reflected general population levels, as asymptomatic patients were undiagnosed and likely unaffected by disease-specific HRQoL. 2.6 | Analysis By running both models, incremental differences in QALYs and healthcare costs between the disrupted care and non-disrupted care situations were estimated across subtypes over a five-year time horizon. QALYs were estimated by multiplying the utility value of each health state by the number of patients in that state per cycle and the cycle length. Summing these values across all cycles yielded the total QALYs. Healthcare costs were calculated similarly, by applying cost estimates to each state and aggregating them over all cycles. To reflect present-day valuations, both healthcare costs and QALYs were discounted annually according to Belgian guidelines—3.0% for costs and 1.5% for health effects (27). Additionally, the models were used to calculate the percentage point differences in mortality and the estimated number of additional deaths attributable to delayed diagnosis across molecular subtypes. 2.7 | Scenario and sensitivity analyses A one-way sensitivity analysis (OSA) and a probabilistic sensitivity analysis (PSA) were performed to assess parameter uncertainty and evaluate the robustness of results across each molecular subtype. The OSA included transition probabilities, utilities, and direct medical costs. Each parameter was varied individually across a range defined by its 95% confidence interval, calculated as the base-case value ± 1.96 × standard error (SE), while holding all other parameters constant. Two tornado diagrams were produced: one showing the impact of varying transition probabilities and utilities on incremental QALYs, and another showing the impact of varying transition probabilities and treatment costs on incremental healthcare costs. Both diagrams reflect results combined across all molecular subtypes. For the PSA, beta distributions were applied to probabilities and utilities, and gamma distributions to costs. Distribution parameters were derived from base-case values and their SEs, calculated from reported sample sizes where available. For parameters without reported variance, plausible 95% ranges (for probabilities/utilities) or an assumed coefficient of variation of 0.10 (for costs) were used to reflect uncertainty. The PSA was conducted using Monte Carlo simulation with 1,000 iterations in Visual Basic for Applications (VBA) within Excel. Three scenario analyses were conducted to assess structural and methodological assumptions. A 10-year time horizon was used to explore longer-term outcomes, particularly early recurrence peaks in HER2 + and TNBC (around two years) and the slower recurrence pattern of luminal A and B (beyond 10 years) (28, 61, 62). The same utility and cost inputs per cycle were maintained, with adapted transition probabilities reflecting recurrence risks beyond five years (28). In the second scenario, we adjusted the base-case 3-month transition probabilities from the UDC state to “Stage I–IV D&T” for each molecular subtype. The base case assumed slower-growing subtypes (e.g., luminal A) were more often diagnosed at early stages, and aggressive subtypes (e.g., TNBC) at later stages, based on clinical evidence(13, 26). This scenario applied subtype-specific multipliers (Table 4) to model more severe progression, increasing late-stage and reducing early-stage diagnoses. As HER2 + and TNBC already had high late-stage probabilities, adjustments were minor, whereas larger multipliers were applied to luminal-like subtypes to test possible underestimation of progression severity. The third scenario examined the impact of changes in radiotherapy reimbursement tariffs during the COVID-19 pandemic. As part of emergency response strategies, several Belgian hospitals adopted hypofractionated radiotherapy (five fractions instead of 15) to reduce hospital visits, minimise infection risks, and optimise resource use(63). This protocol was reimbursed under a higher-cost category (€2,581.73 - nomenclature code: 444710–444721) rather than the standard category III (€1,999.67 in 2020 - nomenclature code: 444150–444161). Belgian data indicate that 8.2% of patients received five fractions in 2020(15). Exploring this scenario is relevant to capture the potential cost implications of shifts in clinical practice and reimbursement during the pandemic. 3 | Results 3.1 | Base case analysis The Markov model estimated that COVID-19 related diagnostic delays in breast cancer across Belgium from March-June 2020, led to a total loss of 20.7 QALYs and €3.19 million in additional healthcare costs over five years, compared to a situation without disruption (Table 6 ). This corresponds to an average loss of €315 per patient and 0.002 QALYs per patient—representing less than one day of life in full health (far below the minimal clinically important difference (MCID) typically cited for oncology, around 0.03 QALYs per year per patient( 64 )). Deterministic model outputs are point estimates derived from fixed parameter values and do not yield confidence intervals; uncertainty in these estimates was instead explored through the PSA. Table 6 Base case: Five-year QALY and cost changes by breast cancer subtype under care disruption. Molecular Subtype Cohort (n) Non-disrupted care (2017–2019) Disrupted-care (2020) Incremental costs & QALYs (Disrupted-care vs. Non-disrupted care) QALYs Cost (€) Death % QALYs Cost (€) Death % ∆QALYs ∆Cost (€) ∆% Point Additional deaths Luminal A Total cohort (n = 5 479) 20 987.5 179 522 108 2.80 20 982.7 180 004 052 2.88 -4.8 481 944 0.08 2 Per patient 3.831 32 765 3.830 32 853 -0.001 88 Luminal B Total cohort (n = 2 740) 10 378.9 103 245 734 4.04 10 375.0 103 770 423 4.09 -4.0 524 689 0.05 1 Per patient 3.788 37 681 3.786 37 872 -0.001 191 HER2+ Total cohort (n = 609) 2 242.4 82 155 748 8.06 2 240.0 82 702 749 8.13 -2.5 547 000 0.07 1 Per patient 3.682 134 903 3.678 135 801 -0.004 898 TNBC Total cohort (n = 1 319) 4 764.4 319 433 158 10.81 4 754.9 321 072 481 11.01 -9.5 1 639 323 0.20 2 Per patient 3.612 242 178 3.605 243 421 -0.007 1 243 Breast cancer Total cohort (n = 10 147) -20.7 3 192 956 Per patient -0.002 315 HER2+: Human Epidermal Growth Factor Receptor 2–positive; QALY: Quality-Adjusted Life Year; TNBC: Triple-Negative Breast Cancer Among molecular subtypes, TNBC accounted for the largest QALY loss (-9.5), followed by luminal A (-4.8), luminal B (-3.9), and HER2+ (-2.5). TNBC patients also incurred the highest additional direct healthcare costs (€1.64 million), followed by HER2+ (€547,000), luminal B (€524,689), and luminal A (€481,944). Per patient, the largest QALY loss was observed in TNBC (-0.007 QALYs, or 2.56 days of full health), followed by HER2+ (-0.004 QALYs, or 1.46 days of full health), and the luminal-like subtypes, both below one day of full health (-0.001 QALYs). TNBC patients incurred the highest additional per-patient costs (€1,243), followed by HER2+ (€898), luminal B (€191), and luminal A (€88). Delayed cancer diagnoses resulted in a slight increase in mortality across all subtypes. The largest absolute increase in mortality occurred in TNBC (+ 0.20 percentage points), resulting in two additional deaths. Luminal A and B subtypes showed increases in mortality of 0.08 and 0.05 percentage points, corresponding to two and one additional death(s), respectively. HER2 + increased by 0.07 percentage points, also leading to one additional death. For TNBC, this corresponds to a 1.9% relative increase compared to non-disrupted care. Luminal A and B showed relative increases of 2.9% and 1.2%, respectively, while HER2 + increased by 0.9%. Relative changes in mortality were calculated as the percentage difference from the non-disrupted care situation. Insert Table 6 here 3.3 | One-Way Sensitivity Analysis (OSA) This section presents the OSA results examining how variations in transition probabilities, costs, and utilities influence both changes in incremental QALYs and healthcare costs. Two tornado diagrams (Figs. 2 and 3 ) are provided to illustrate the parameters driving the greatest changes. 3.3.1 | The impact of varying transition probabilities and utilities on incremental QALYs The OSA on QALYs showed that the model was most sensitive to changes in the utilities for TNBC. Particularly, utilities for stage I D&T, stage I–III adjuvant therapy, stage IV D&T, and stage II D&T had the largest impact on incremental QALYs. Figure 2 . Tornado diagram showing the impact of varying transition probabilities and utilities on incremental QALYs across breast cancer molecular subtypes L: Lines of systemic therapy (2L: second therapy line; 3L: third therapy line; 4L: fourth therapy line); LRR: Locoregional recurrence; LUM A: Luminal A; LUMA B: Luminal B; QALY: Quality-Adjusted Life Year; TNBC: Triple-negative breast cancer; TP: Transition probabilities; UDC: Undiagnosed cancer *The vertical dashed line represents the base case incremental QALY result. Dark grey bars indicate the change when the parameter is set to its lower bound, and light grey bars indicate the change when set to its upper bound. 3.3.2 | The impact of varying model inputs on incremental healthcare cost changes The OSA on healthcare costs showed that the model was most sensitive to cost variations in TNBC. Specifically, the costs of stage I D&T and stage IV D&T, which had the largest impacts on total incremental healthcare costs. Figure 3 . Tornado diagram showing the impact of varying transition probabilities and treatment costs on incremental healthcare costs across breast cancer molecular subtypes. D&T: Diagnosis and treatment; L: Lines of systemic therapy (2L: second therapy line; 3L: third therapy line; 4L: fourth therapy line); LRR: Locoregional recurrence; LUM A: Luminal A; LUMA B: Luminal B; TNBC: Triple-negative breast cancer; TP: Transition probabilities; UDC: Undiagnosed cancer *The vertical dashed line represents the base case incremental healthcare cost result. Dark grey bars indicate the change when the parameter is set to its lower bound, and light grey bars indicate the change when set to its upper bound. 3.4 | Scenario analyses Extending the time horizon from five to 10 years revealed a more pronounced impact of COVID-19-related disruptions on both health and economic outcomes (Table 7 ). QALY losses widened from − 20.7 to -32.1, and incremental healthcare costs rose by €2.50 million (from €3.19 million to €5.69 million). Luminal-like subtypes experienced over a twofold increase in both QALY losses and healthcare costs, whereas increases were less pronounced for TNBC and HER2+: QALY losses doubled for HER2 + but fell by more than half for TNBC, while costs rose by over 1.5-fold for both. Mortality trends were consistent with the base case, with TNBC showing the largest absolute increase (+ 0.33 percentage points), followed by HER2+ (+ 0.11), luminal B (+ 0.06), and luminal A (+ 0.04). These corresponded to relative increases in mortality of 1.6% for TNBC (from 20.44% to 20.77%), 0.8% for HER2+ (from 13.64% to 13.75%), 0.7% for luminal B (from 9.10% to 9.16%), and 0.6% for luminal A (from 6.36% to 6.40%). Table 7 Scenario analysis: Incremental changes in QALYs and healthcare costs across three scenarios, by molecular subtype. Scenario type Molecular Subtype Cohort (n) Non-disrupted care (2017–2019) Disrupted care (2020) Incremental healthcare costs & QALYs (Disrupted care vs. Non-disrupted care) QALYs Cost (€) Death % QALYs Cost (€) Death% ∆QALYs ∆Cost (€) ∆% Point Additional deaths 10-year time horizon Luminal A Total 39 531.7 342 044 246 6.36 39 517.6 343 230 607 6.40 -13.1 1 186 360 0.04 3 Per patient 7.215 62 428 7.213 62 645 -0.002 217 Luminal B Total 19 182.2 228 963 281 9.10 19 172.2 230 020 328 9.16 -10.0 1 057 047 0.06 2 Per patient 7.001 83 563 6.997 83 949 -0.004 386 HER2+ Total 4 103.1 128 324 067 13.64 4 097.5 129 197 648 13.75 -5.5 873 581 0.11 1 Per patient 6.737 210 713 6.728 212 147 -0.009 1,434 TNBC Total 8 538.1 431 548 773 20.44 8 534.6 434 122 775 20.77 -3.4 2 574 002 0.33 4 Per patient 6.473 327 179 6.471 329 130 -0.003 1 951 Breast Cancer Total -32.1 5 690 990 Per patient -0.003 561 Shift to advanced stages for the UDC patients Luminal A Total 20 987.5 179 522 108 2.80 20 972.4 184 012 139 2.88 -15.2 4 490 031 0.08 5 Per patient 3.831 32 765 3.828 33 585 -0.003 819 Luminal B Total 10 378.9 103 245 734 4.04 10 371.2 105 063 870 4.13 -7.7 1 818 135 0.09 2 Per patient 3.788 37 681 3.785 38 344 -0.003 664 HER2+ Total 2 242.4 82 155 748 8.06 2 239.5 82 838 914 8.14 -2.9 683 165 0.08 1 Per patient 3.682 134 903 3.677 136 024 -0.005 1 122 TNBC Total 4 764.4 319 433 158 10.81 4 753.3 321 443 662 11.03 -11.1 2 010 504 0.22 3 Per patient 3.612 242 178 3.604 243 703 -0.008 1 524 Breast Cancer Total -36.9 9 001 835 Per patient -0.004 887 Shift in radiotherapy costs during COVID-19 Luminal A Total 20 987.5 179 522 108 2.80 20 982.7 180 740 027 2.88 -4.8 1 217 920 0.08 2 Per patient 3.831 32 765 3.830 32 988 -0.001 222 Luminal B Total 10 378.9 103 245 734 4.04 10 375.0 104 136 793 4.09 -3.9 891 059 0.05 1 Per patient 3.788 37 681 3.786 38 006 -0.001 325 HER2+ Total 2 242.4 82 155 748 8.06 2 240.0 82 856 543 8.13 -2.5 700 794 0.07 1 Per patient 3.682 134 903 3.678 136 053 -0.004 1 151 TNBC Total 4 764.4 319 433 158 10.81 4 754.9 321 413 901 11.01 -9.5 1 980 743 0.20 2 Per patient 3.612 242 178 3.605 243 680 -0.007 1 502 Breast Cancer Total -20.7 4 790 516 Per patient -0.002 472 HER2+: Human Epidermal Growth Factor Receptor 2–positive; QALY: Quality-Adjusted Life Year; TNBC: Triple-Negative Breast Cancer; UDC: Undiagnosed Cancer Applying increased transition probabilities through multipliers (Table 4 ) from the UDC state towards advanced stage at diagnosis (i.e., stage III and IV) across subtypes resulted in nearly 37 QALYs lost and €9 million in additional healthcare costs, almost a threefold increase compared to the base case (Table 6 ). Luminal A and B subtypes incurred €4.49 million and €1.82 million in costs, and lost 15.17 and 7.70 QALYs, respectively. TNBC costs increased by €371,181 (to €2.01 million), and HER2 + by €136,165 (to €683,165). This scenario projected 11 additional deaths—almost twice the number in the base case—mainly among luminal subtypes. Absolute mortality increases were highest in TNBC (+ 0.22 percentage points), followed by luminal A and HER2+ (+ 0.08 each) and luminal B (+ 0.09). These corresponded to relative mortality increases of 2.0% for TNBC (from 10.81% to 11.03%), 2.9% for luminal A (from 2.80% to 2.88%), 2.2% for HER2+ (from 8.06% to 8.14%), and 2.2% for luminal B (from 4.04% to 4.13%). When assuming that 8.2% of patients received a 5-fraction radiotherapy regimen( 15 )—costlier than the standard 15-fraction course—during the COVID-19 period, and that this practice persisted over five years, the model projected an increase of €1.60 million (to €4.79 million) in incremental breast cancer costs across all subtypes. This translated into a 2.5-fold increase for luminal A, 1.7-fold for luminal B, 1.3-fold for HER2+, and 1.2-fold for TNBC. Insert Table 7 here 3.5 | Probabilistic Sensitivity Analysis (PSA) Scatterplots of the 1,000 PSA iterations were generated for each subtype to illustrate the distribution of incremental QALYs and healthcare costs (Figs. 2 – 5 ). Table 8 presents the mean outcomes from the PSA by molecular subtype, including incremental QALYs and healthcare costs, as well as the corresponding 95% CIs. Across all molecular subtypes, the PSA revealed wide uncertainty in incremental QALYs and costs. For luminal A, the 95% CI for incremental QALYs ranged from − 1,255.36 to 1,249.99, with a 51% probability of QALY loss, while incremental healthcare costs ranged from -€26,790,493 to €24,962,257, with a 52% probability of cost increase (Fig. 2 ). For luminal B, QALY changes ranged from − 625.65 to 604.00 (49% probability of loss) and costs from -€19,523,679 to €19,107,800 (50% probability of increase) (Fig. 3 ). For HER2+, QALYs ranged from − 160.11 to 154.10 (50% probability of loss) and costs from -€16,775,684 to €16,469,399 (52% probability of increase) (Fig. 4 ). For TNBC, QALYs ranged from − 213.03 to 200.42 (54% probability of loss) and costs from -€47,871,357 to €53,229,055 (51% probability of increase) (Fig. 5 ). These ranges illustrate that, while point estimates align with base-case findings (Table 6 ), the CIs encompass both potential gains and losses in QALYs as well as cost savings and increases. Table 8 Probabilistic sensitivity analysis: QALY and cost changes by breast cancer subtype under care disruption. Molecular Subtype Breast cancer cohort Non-disrupted care (2017–2019) Disrupted care (2020) Incremental costs & QALYs (Disrupted care vs. non-disrupted care) QALYs Cost ( €) QALYs Cost ( €) ∆QALYs ∆Cost ( €) Luminal A Total 95% CI 20 991.0 [20 962.2 - 21 019.8] 179 335 236 [178 767 318 - 179,903 154) 21 001.1 [20 972.8 - 21 029.3] 179 752 285 [179 187 369 - 180 317 201] -15.2 [-1 255.4–1 249.99] 417 049 [-26 790 493–24 962 257] Luminal B Total 95% CI 10 375.6 [10 361.8 - 10 389.4] 103 543 022 [103 113 677 - 103 972 368] 10 373.5 [10 360.0 - 10 386.9] 103 641 533 [103 219 807 - 104 063 259] -2.1 [-625.7–604.0] 98 511 [-19 523 679–19 107 800] HER2+ Total 95% CI 2 243.4 [2 240.2 - 2 246.6] 82 244 443 [81 876 267 - 82 612 619] 2 242.3 [2 238.9 – 2 245.7] 82 474 170 [82 126 409 – 82 812 930] -1.0 [-160.1–154.1] 229 727 [-16 775 684–16 469 399] Triple-Negative Total 95% CI 4 768.7 [4 762.5 - 4 775.0] 319 960 341 [318 901 500 - 321 019 181] 4 757.1 [4 754.1 - 4 760.0] 321 551 351 [320 410 320 - 322 692 381] -11.7 [-213.0–200.4] 1 591 010 [-47 871 357–53 229 055] HER2+: Human Epidermal Growth Factor Receptor 2–positive; QALY: Quality-Adjusted Life Year; 95% CI: 95% confidence interval QALY: Quality-Adjusted Life Year; 95% CI: 95% confidence interval Note The red solid line represents the mean value across simulations. Black dashed lines indicate the 95% CIs. The shaded grey area marks the CI range. Percentages refer to the proportion of simulations showing gains vs. losses (A) or cost increases vs. savings (B). QALY: Quality-Adjusted Life Year; 95% CI: 95% confidence interval Note The red solid line represents the mean value across simulations. Black dashed lines indicate the 95% CIs. The shaded grey area marks the CI range. Percentages refer to the proportion of simulations showing gains vs. losses (A) or cost increases vs. savings (B). QALY: Quality-Adjusted Life Year; 95% CI: 95% confidence interval Note The red solid line represents the mean value across simulations. Black dashed lines indicate the 95% CIs. The shaded grey area marks the CI range. Percentages refer to the proportion of simulations showing gains vs. losses (A) or cost increases vs. savings (B). QALY: Quality-Adjusted Life Year; 95% CI: 95% confidence interval Note The red solid line represents the mean value across simulations. Black dashed lines indicate the 95% CIs. The shaded grey area marks the CI range. Percentages refer to the proportion of simulations showing gains vs. losses (A) or cost increases vs. savings (B). 4 | Discussion This modelling exercise projected that breast cancer diagnostic delays between March and June 2020 in Belgium had only a modest health and economic impact over five years. Among 10,147 patients, 135 undiagnosed cases were estimated to result in 21 QALYs lost (around 0.002 per patient, which is less than one day in full health and far below the MCID of 0.03 per patient per year ( 64 )), six additional deaths, and €3.2 million in extra healthcare costs (1% of Belgian annual breast cancer expenditure). From a public health perspective, the disruption’s impact was limited and smaller than expected, despite early reports and widespread concern( 65 – 67 ) during the first pandemic months. Diagnostic delays affect all breast cancers, but their impact depends on tumour biology: missing an aggressive subtype such as TNBC or HER2 + carries greater risk than delaying diagnosis of luminal-like cancers( 26 ). In line with this rationale, our model applied clinically supported but hypothesised progression assumptions, which projected heterogeneity by subtype: TNBC and HER2 + together accounted for nearly 70% of excess healthcare costs and 60% of QALY losses. Extending the time horizon to 10 years yielded similarly limited impacts, with over 32 QALYs lost—equivalent to around one day of full health per patient—and incremental healthcare costs representing < 2% (€5.7 million) of Belgian annual breast cancer expenditure. The relatively larger incremental QALY and cost impacts for luminal-like subtypes reflect their longer recurrence trajectories( 28 , 61 ), whereas most adverse outcomes for HER2 + and TNBC, which recur earlier( 28 , 61 ), were already captured within the five-year horizon. Even when assuming higher probabilities of UDC being diagnosed at advanced stages across all subtypes, the projected 5-year impact remained modest, with 37 QALYs lost (under two days in full health per patient) and €9 million in incremental costs (< 3% of annual breast cancer expenditure). Although the deterministic base-case analysis projected modest incremental effects, the PSA revealed that these estimates are highly uncertain. Across all molecular subtypes, the 95% CIs spanned both positive and negative values, with probabilities of QALY loss or cost increase close to 50%. This indicates that the base-case results represent only one possible outcome within a wide distribution: small changes in key parameters could plausibly result in either greater harm or no adverse effect at all. Much of this uncertainty stems from transition probabilities from UDC to stage-specific diagnosis and from utility estimates, particularly for TNBC and HER2 + where data were limited. Consistent with this, the OSA identified TNBC inputs—with especially wide 95% CI—as having the greatest influence on overall results. The robustness of conclusions therefore hinges on the precision of such parameters, underscoring the need for higher-quality, subtype-specific data to reduce uncertainty. The limited incremental QALYs and healthcare costs are likely explained by the small proportion of UDC (1.33%) reported at the end of 2020, which were then modelled over a five-year period. Indeed, even though a larger proportion of patients were undiagnosed during the first few months of the pandemic( 15 ), the majority was recovered by year-end. This is linked to Belgium’s screening programmes that resumed in June 2020 and ‘do-not-delay-care’ campaigns( 68 ), proactive primary-care outreach( 69 ) and teleconsultation policies( 70 ), which supported timely symptomatic presentation. Consistent with this, a Belgian observational study ( 15 ) found no evidence of a national stage shift, tumour size increase, or nodal progression in 2020, with DCIS diagnoses—likely most of the missing cases—rebounding by late 2020 and screening volumes recovering in 2021. Our study is not the first to examine the potential health and economic consequences of COVID-19–related diagnostic delays in breast cancer. Sud et al. ( 71 ) modelled delays in the UK’s 2-week-wait referral pathway applying per-day hazard ratios for treatment delay uniformly to all stage I–III cases diagnosed through this route, which projected 733 excess breast cancer deaths and 15,339 life-years lost annually in England from a three-month delay. Maringe et al.,( 21 ) using linked NHS registry and hospital data in a stage-shift model, estimated 281–344 additional breast cancer deaths over five years in England due to pandemic-related diagnostic delays. Gheorghe et al.( 25 ) then translated these mortality projections into 4,100 QALYs lost and £23.2 million in productivity losses. Degeling et al.( 72 ), applying an Australian stage-shift model, projected 20–64 life-years lost and AU $ 2.0–7.7 million in extra costs for breast cancer under 3–6 month delay scenarios. Differences between our findings and those of previous modelling studies can be explained by several factors. First, population sizes and healthcare systems differ between Belgium, the UK, and Australia, likely influencing the scale of the impact. Belgium’s smaller population, dense hospital network, and high acute bed capacity( 73 ) supported resilience during the first wave, while rapid measures—such as reimbursed teleconsultations( 74 ), proactive primary care outreach, adapted cancer guidelines( 75 ), and hypofractionated radiotherapy( 15 )—helped sustain continuity of diagnosis and treatment. Importantly, breast cancer screening resumed in June 2020( 15 ), earlier than in parts of the UK( 76 , 77 ), which reduced the period of diagnostic delay. Coupled with strong inter-hospital collaboration( 78 ) and national ‘do-not-delay-care’ campaigns, these factors limited the proportion of undiagnosed cases that progressed to later stages. This helps explain why our model projected smaller QALY losses, lower additional healthcare costs, and fewer excess deaths compared with modelling studies from the UK and Australia, where screening interruptions persisted longer and system capacity was more constrained. Second, prior models varied substantially in design: Sud et al.( 71 ) applied per-day hazards of delay across all stage I–III cancers diagnosed via the 2-week-wait pathway; Maringe et al.( 21 ) modelled large shifts from routine or screening routes to emergency presentations; Gheorghe et al.( 25 ) translated Maringe et al.( 21 ) excess deaths into QALY and productivity losses using a societal human-capital approach; and Degeling et al.( 72 ) applied an inverse stage-shift method, conservatively modelling stage I to II progression under uniform 3–6 month delays. By contrast, our subtype-specific Markov model compared two Belgian cohorts: the observed 2020 breast cancer population (with 1.33% UDC) and a projected ‘no disruption’ cohort based on 2017–2019 trends, with UDC progression under diagnostic delay hypothesised to stage-specific diagnoses according to molecular subtype. Third, the duration and intensity of disruption differed markedly: Belgian screening was suspended for approximately three months with rapid recovery, whereas UK scenarios assumed screening disruptions lasting up to 12 months( 71 ). Finally, our analysis adopted a healthcare payer perspective, excluded DCIS and unknown-stage cases, and assumed constant treatment trajectories. These choices, combined with Belgium’s mitigation measures and the relatively rapid resumption of screening( 15 )—in contrast to Scotland( 77 ) and Wales( 76 ) for instance, where screening remained suspended until August 2020—may help explain the smaller health and economic impacts observed. 4.1 | Strengths and limitations The primary strength of this study lies in its use of a Markov model that integrates Belgian observational data on breast cancer incidence by clinical stage. To our knowledge, this is the first subtype-specific Markov model assessing the health and economic impact of COVID-19–related breast cancer diagnostic delays in Belgium. Despite uncertainties in input parameters, robust sensitivity analyses and expert consultations strengthened the model’s credibility and ensured that limitations were explicitly addressed. The framework is adaptable to other countries, providing a versatile policy tool ( 29 ). Several limitations should be noted. While stage-specific projections from the BCR enabled estimation of UDC by stage in 2020, data were not subtype-specific. Applying the overall stage distribution uniformly across subtypes may overestimate the contribution of aggressive cancers, which are less likely to remain undiagnosed, while underestimating their impact, as missed cases would more plausibly present at later stages. A related limitation is that the model assumed equal probability of missed diagnoses across stages, although most undiagnosed cases were likely early-stage, screen-detected tumours. Some input parameters—such as transition probabilities from UDC to stage-specific diagnosis by subtype and selected disutilities (Table 4 )—were not empirically available and were instead specified as plausible assumptions informed by clinical evidence. While this approach enabled model implementation, it inevitably introduces additional uncertainty in the incremental outcomes, as reflected in the PSA. Simplifying treatment trajectories (e.g., equating costs and utilities between luminal subtypes) may have attenuated subgroup-specific differences, producing more moderate aggregate results than would be expected if full clinical heterogeneity were incorporated. While age is a key prognostic factor and BCR data are available by age group, the absence of stratification by subtype prevented the model from accounting for age-related differences in progression, treatment response, or survival within subtypes—for instance, potentially larger QALY and healthcare cost impacts among younger women with aggressive cancers, and smaller impacts among older women due to higher competing mortality and less intensive treatment. The model assumed identical input parameters for disrupted and non-disrupted care, except for UDC and related transition probabilities. A third scenario incorporating Belgian data on radiotherapy reimbursement( 79 ) (5-fraction vs. 15-fraction) showed that altering a single parameter could change incremental costs. Other adaptations—telemedicine( 70 ), early discharge( 80 ), and organizational changes—also likely affected costs and outcomes in both directions, so cumulative unmodelled effects may have differed from our estimates. Finally, certain clinical aspects were not modelled to avoid added complexity—for example, recurrence risks vary by stage and subtype, but LRR within stages I–III was not modelled separately. 4.2 | Policy implications and future research Although concerns during the initial pandemic suggested severe consequences, our analysis and BCR data indicate that the early disruption of breast cancer diagnosis had a modest five-year impact that, while growing slightly when projected over a 10-year horizon, remained limited overall. This likely reflects the rapid recovery of screening, proactive GP outreach, and targeted “do-not-delay-care” campaigns, which helped avoid a sustained stage shift. For policy, these findings highlight the need to protect continuity of diagnostic services during system shocks, minimise backlogs once services resume, and ensure vigilance for aggressive tumours such as TNBC and HER2 + by providing subtype-specific incidence data. Future work should build on these insights by maintaining registry-based monitoring to capture possible longer-term impacts, particularly in aggressive subtypes, and by incorporating outcomes such as recurrence, overall survival, and HRQoL. An important next step will be for cancer registries to routinely provide subtype-specific incidence and stage data, enabling more granular assessment of differential impacts and supporting robust, policy-relevant modelling. It will also be important to evaluate the cumulative impact of multiple pandemic waves and to assess the effects of treatment adaptations (e.g., telemedicine, altered radiotherapy regimens, early discharge) that may have influenced QALY and healthcare cost changes. Finally, this study illustrates the value of rapid modelling exercises as a complement to slower-to-emerge observational data, providing policymakers with timely evidence to guide mitigation strategies and strengthen preparedness for future health system disruptions. 4.3 | Conclusion This study shows that breast cancer diagnostic delays during Belgium’s first COVID-19 wave had modest five-year consequences: 21 QALYs lost, six additional deaths, and €3.2 million in costs (< 1% of annual expenditure). These smaller-than-expected impacts are reassuring and likely reflect Belgium’s rapid screening recovery and mitigation measures, which prevented a sustained stage shift. Subtype-specific modelling, however, revealed that aggressive cancers such as TNBC and HER2 + are more delay-sensitive than luminal-like subtypes. As projections were strongly assumption-driven and highly sensitive to uncertain inputs, higher-quality, subtype-specific data are needed. Ongoing monitoring of recurrence, survival, and HRQoL remains crucial to capture longer-term effects and guide policies that ensure diagnostic continuity and prioritise high-risk subtypes during future disruptions. Abbreviations AT: Adjuvant Therapy BCPI: Belgian Centre for Pharmacotherapeutic Information BCR: Belgian Cancer Registry BCS: Breast-Conserving Surgery CDK4/6: Cyclin-Dependent Kinases 4 and 6 COVID-19: Coronavirus Disease 2019 D&T: Diagnosis and Treatment DCIS: Ductal Carcinoma In Situ ER: Estrogen Receptor ESMO: European Society for Medical Oncology FU: Follow-Up HER2: Human Epidermal Growth Factor Receptor 2 HRQoL: Health-Related Quality of Life LRR: Locoregional Recurrence MCID: Minimal Clinically Important Difference MST: Mastectomy NIHDI: National Institute for Health and Disability Insurance OSA: One-Way Sensitivity Analysis PR: Progesterone Receptor PSA: Probabilistic Sensitivity Analysis QALY: Quality-Adjusted Life Year RT: Radiotherapy SE: Standard Error TNBC: Triple-Negative Breast Cancer UDC: Undiagnosed Cancer VBA: Visual Basic for Applications Declarations Ethics approval and consent to participate: This study was based on aggregated, de-identified registry data from the Belgian Cancer Registry (BCR), which exempted it from ethical review. Consent for publication: Not applicable. Availability of data and materials: The Excel-based Markov model developed and analysed during the current study is provided as supplementary material with this article. In addition, a Word technical appendix (Additional file 1) is provided, containing detailed descriptions of the model structure, transition probabilities, utilities, healthcare costs, and assumptions used in the analysis. Competing interests: The authors declare that they have no competing interests. Funding: This work was supported by Belgian Federal Science Policy Office, B2/202/P3/HELICON. The funder had no role in study design, analysis, interpretation, or manuscript writing. Authors' contributions: YK conceived and designed the study, developed the Markov model, collected data inputs, performed the analyses, and drafted the manuscript. NV supervised the economic modelling, and critically reviewed the manuscript. 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Additional Declarations No competing interests reported. Supplementary Files BCMolecularSubtypesMarkovModel.xlsx Additionalfile1.docx Format: .docx Title: Technical appendix for the breast cancer Markov model Description: Provides detailed model structure, parameter inputs, transition probabilities, health-state utilities, cost calculations, and assumptions used in the analysis. Cite Share Download PDF Status: Published Journal Publication published 28 Dec, 2025 Read the published version in Breast Cancer Research → Version 1 posted Editorial decision: Revision requested 21 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Reviewers invited by journal 17 Oct, 2025 Editor assigned by journal 07 Oct, 2025 Submission checks completed at journal 07 Oct, 2025 First submitted to journal 05 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7786399","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535858556,"identity":"982d7b65-e603-49af-8647-0b674da5915e","order_by":0,"name":"Yasmine 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12:10:09","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":359438,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7786399/v1/9cb2424ad72007dcc96a0a52.html"},{"id":94762213,"identity":"ce1d00d5-89e2-44d1-8ecd-9e7dec31d7f4","added_by":"auto","created_at":"2025-10-30 12:10:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":125657,"visible":true,"origin":"","legend":"\u003cp\u003eBreast cancer Markov model diagram with 11 health states, applicable to each molecular subtypes.\u003cbr\u003e\n \u003cem\u003eD \u0026amp; T: Diagnosis and treatment; FU: Follow-up; L: Lines of systemic therapy; LRR: Locoregional recurrence\u003cbr\u003e\nL: Lines of systemic therapy; UDC : Undiagnosed cancer \u003cbr\u003e\n ↺ = Probability of remaining in the state\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e→ = Probability of moving to another state\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e] = Probability for patients in any state to move to the dead state\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7786399/v1/e4ce39626c5c3fee4b7d7140.png"},{"id":94762214,"identity":"a96936d9-a26a-45fa-bca7-b2788adc49e5","added_by":"auto","created_at":"2025-10-30 12:10:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":258622,"visible":true,"origin":"","legend":"\u003cp\u003eTornado diagram showing the impact of varying transition probabilities and utilities on incremental QALYs across breast cancer molecular subtypes\u003cbr\u003e\n \u003cem\u003eL: Lines of systemic therapy (2L: second therapy line; 3L: third therapy line; 4L: fourth therapy line); LRR: Locoregional recurrence; LUM A: Luminal A; LUMA B: Luminal B; QALY: Quality-Adjusted Life Year; TNBC: Triple-negative breast cancer; TP: Transition probabilities; UDC: Undiagnosed cancer\u003cbr\u003e\n*The vertical dashed line represents the base case incremental QALY result. Dark grey bars indicate the change when the parameter is set to its lower bound, and light grey bars indicate the change when set to its upper bound.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7786399/v1/34e81a2adb86738ea515b372.png"},{"id":94824368,"identity":"4cdda886-45eb-4884-bf3a-ba00d02a70b4","added_by":"auto","created_at":"2025-10-31 06:48:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":258659,"visible":true,"origin":"","legend":"\u003cp\u003eTornado diagram showing the impact of varying transition probabilities and treatment costs on incremental healthcare costs across breast cancer molecular subtypes.\u003cstrong\u003e\u003cbr\u003e\n \u003c/strong\u003e\u003cem\u003eD\u0026amp;T: Diagnosis and treatment; L: Lines of systemic therapy (2L: second therapy line; 3L: third therapy line; 4L: fourth therapy line); LRR: Locoregional recurrence; LUM A: Luminal A; LUMA B: Luminal B; TNBC: Triple-negative breast cancer; TP: Transition probabilities; UDC: Undiagnosed cancer\u003cbr\u003e\n*The vertical dashed line represents the base case incremental healthcare cost result. Dark grey bars indicate the change when the parameter is set to its lower bound, and light grey bars indicate the change when set to its upper bound.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7786399/v1/f215b7f9b484974caa1acc66.png"},{"id":94823974,"identity":"083ec381-479f-4fc6-86b6-60a1a97dceda","added_by":"auto","created_at":"2025-10-31 06:48:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":421455,"visible":true,"origin":"","legend":"\u003cp\u003eIncremental QALYs (A) and healthcare costs (B) for the total HER2+ cohort: Results from 1,000 PSA iterations.\u003cem\u003e\u003cbr\u003e\nQALY: Quality-Adjusted Life Year; 95% CI: 95% confidence interval \u003cbr\u003e\nNote. The red solid line represents the mean value across simulations. Black dashed lines indicate the 95% CIs. The shaded grey area marks the CI range. Percentages refer to the proportion of simulations showing gains vs. losses (A) or cost increases vs. savings (B).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7786399/v1/a0b8cec0c5cd1c0114e32e56.png"},{"id":94762218,"identity":"06150238-750d-4567-8fbd-d56fa1ce3c3f","added_by":"auto","created_at":"2025-10-30 12:10:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":404496,"visible":true,"origin":"","legend":"\u003cp\u003eIncremental QALYs (A) and healthcare costs (B) for the total TNBC cohort: Results from 1,000 PSA iterations.\u003cem\u003e\u003cbr\u003e\nQALY: Quality-Adjusted Life Year; 95% CI: 95% confidence interval\u003cbr\u003e\nNote. The red solid line represents the mean value across simulations. Black dashed lines indicate the 95% CIs. The shaded grey area marks the CI range. Percentages refer to the proportion of simulations showing gains vs. losses (A) or cost increases vs. savings (B).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7786399/v1/3728aca4a4d79b35ebaff2a2.png"},{"id":99172220,"identity":"0026dde9-fe38-4a8c-97d8-029273106194","added_by":"auto","created_at":"2025-12-29 16:03:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4251656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7786399/v1/f9835c20-9b05-4149-b2b2-0b09fecd1b98.pdf"},{"id":94762236,"identity":"c8c8baf9-5e21-4f90-bf7f-b5b780d5f60d","added_by":"auto","created_at":"2025-10-30 12:10:09","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5859482,"visible":true,"origin":"","legend":"","description":"","filename":"BCMolecularSubtypesMarkovModel.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7786399/v1/e882414fb4ed403cfab0e38b.xlsx"},{"id":94762220,"identity":"7e938d74-c403-44bd-9f26-d0755d9fa3ed","added_by":"auto","created_at":"2025-10-30 12:10:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":112864,"visible":true,"origin":"","legend":"\u003cp\u003eFormat: .docx\u003c/p\u003e\n\u003cp\u003eTitle: Technical appendix for the breast cancer Markov model\u003c/p\u003e\n\u003cp\u003eDescription: Provides detailed model structure, parameter inputs, transition probabilities, health-state utilities, cost calculations, and assumptions used in the analysis.\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7786399/v1/30d3c3df7068fa2503365c88.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Subtype-specific health and economic impact of delayed breast cancer diagnosis during the early COVID-19 pandemic in Belgium: A Markov model analysis","fulltext":[{"header":"1 | BACKGROUND","content":"\u003cp\u003eCancer remains a leading cause of morbidity and mortality worldwide, placing a substantial health and economic burden on societies. In Europe alone, total cancer-related costs were estimated at \u0026euro;199\u0026nbsp;billion in 2018(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), including direct medical expenses, indirect costs (e.g., productivity losses), and direct non-medical costs like informal caregiving. Breast cancer is the most prevalent cancer among women and the second costliest cancer globally(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Belgium has among the highest age-standardized breast cancer incidence rates in the world(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), contributing substantially to the national healthcare burden. In 2018, breast cancer healthcare costs exceeded \u0026euro;300\u0026nbsp;million(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), driven by its high incidence\u0026mdash;11,636 new cases in 2023(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u0026mdash;and an aging population, which increases disease risk and leads to more complex care (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Despite the high costs, breast cancer has a relatively low burden of years lived with disability per case(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This is largely due to early detection\u0026mdash;often through organized screening programs\u0026mdash;and effective treatment, which improve prognosis and reduce recurrence and long-term disability(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These outcomes are further supported by Belgium\u0026rsquo;s structured breast cancer care pathway, with accredited clinics, mandatory multidisciplinary consultations, and national standards for timely, coordinated, high-quality care(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDuring the first wave of COVID-19, many countries implemented strict public health measures, including lockdowns, social distancing, and healthcare resource reallocation, to contain the spread of the virus(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). These led to suspension of non-urgent services and disrupted breast cancer care globally, with halted screening and adapted treatments to reduce exposure and preserve capacity(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). There was also concern that patients delayed seeking care due to fear of infection, healthcare strain, or a desire not to burden the system(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Delayed breast cancer care could theoretically lead to more advanced-stage presentations, resulting in higher treatment costs, reduced health-related quality of life (HRQoL), and poorer survival(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Belgium, the organized breast cancer screening programme was suspended from March to June 2020, causing a 44% drop in Ductal Carcinoma In Situ (DCIS) diagnoses(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). As DCIS is mainly detected through screening, this decline reflects programme disruption, though some were still found symptomatically. By year-end, 5% of breast cancer cases\u0026mdash;both DCIS and invasive\u0026mdash;remained undiagnosed despite screening resumption\u0026sup1;\u0026sup1;. Importantly, Belgian data show that time to treatment was largely maintained or even accelerated, with no increase in tumour size or nodal involvement among symptomatic cases(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBreast cancer is a heterogenous disease with multiple subtypes that differ in biological behaviour and clinical outcomes. The most widely used classification system relies on immunohistochemical profiling of estrogen, progesterone, and human epidermal growth factor 2 (HER2) receptors. Based on these markers, breast cancers are commonly grouped into four subtypes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): luminal A, luminal B, HER2-positive (HER2+), and triple-negative (TNBC). This classification guides treatment decisions and reflects differences in prognosis and response to therapy(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Luminal A cancers tend to grow slowly and have a favourable prognosis, whereas HER2\u0026thinsp;+\u0026thinsp;and TNBC subtypes are more aggressive, with faster progression and worse outcomes if diagnosis is delayed(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In Belgium, data from 2014 show that luminal A-like tumours accounted for 54% of cases, followed by luminal B-like (27%), TNBC (13%), and HER2+ (6%) breast cancers(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe impact of a temporary disruption of cancer screening on HRQoL, healthcare costs, and mortality across breast cancer molecular subtypes in Belgium remains unknown. However, evidence from other countries indicates that even short-term screening interruptions may result in more advanced-stage diagnoses(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), higher projected mortality(\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u0026mdash;particularly among aggressive subtypes\u0026mdash;and substantial long-term HRQoL and economic burdens(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). A recent study(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) found that even short diagnostic delays can lead to more advanced cancer, especially for aggressive subtypes. For TNBC, a delay of 38 days caused more than 10% of patients to be diagnosed at a later stage. This window was 52 days for HER2\u0026thinsp;+\u0026thinsp;cancers but a longer 85 days for slower-growing luminal-like cancers, highlighting the heightened sensitivity of aggressive tumours to diagnostic delays.\u003c/p\u003e\u003cp\u003eAlthough Belgian data did not indicate tumour or nodal progression among breast cancer cases with delayed diagnosis between March and June 2020, these analyses were not stratified by molecular subtype(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). As such, potential stage progression in aggressive subtypes, such as TNBC or HER2\u0026thinsp;+\u0026thinsp;cancers, may have been masked by the predominance of slower-growing tumours like luminal A.\u003c/p\u003e\u003cp\u003eThrough a Markov cohort simulation model, we explore the potential hidden impact of these diagnostic delays on changes in Quality-Adjusted Life Years (QALYs) and healthcare costs, across molecular subtype, compared to pre‑COVID‑19 care conditions. In line with Belgian guidelines for health economic evaluations(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), these standard metrics were selected to capture the comprehensive patient health burden and the direct economic impact on the healthcare system, respectively. We projected the impact over a five-year horizon, as this is the clinically critical period when the risk of cancer recurrence is highest and the negative health and economic consequences of a delayed diagnosis are most likely to emerge(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This timeframe is also supported by the most robust clinical evidence on treatment outcomes and costs(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). These findings aim to provide healthcare professionals and policymakers with evidence to safeguard continuity of diagnostic services during future system shocks, minimise backlogs once services resume, prioritise timely detection of aggressive subtypes such as TNBC and HER2\u0026thinsp;+\u0026thinsp;cancers, and encourage further breast cancer research stratified by molecular subtype.\u003c/p\u003e"},{"header":"2 | METHODS","content":"\u003cp\u003eWe used a Markov cohort simulation model approach, as described in our protocol (29), to project over five years the health and healthcare cost impacts of COVID-19-attributable breast cancer diagnostic disruptions that occurred from mid-March to June 2020 in Belgium. Markov models reflect well breast cancer’s chronic nature, stage progression, and long-term recurrence and mortality risk(30). The analysis was stratified by four breast cancer molecular subtypes (Table\u0026nbsp;1): luminal A, luminal B, HER2+, and TNBC.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCharacteristics of the four breast cancer molecular subtypes (18)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMolecular subtype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReceptor status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBiological behaviour\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrognosis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eER+, PR+, HER2–, low Ki-67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlow-growing, hormone-sensitive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFavourable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndocrine therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eER+, PR±, HER2– or HER2+, high Ki-67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore proliferative than Luminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndocrine therapy + CDK4/6 inhibitors, ± chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+, ER–/low, PR–/low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighly proliferative, treatment-sensitive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable (improved with HER2-targeted therapy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-HER2 therapy (e.g., trastuzumab, pertuzumab) + chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eER–, PR–, HER2–\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv\u003e\n \u003cp\u003eHighly aggressive, rapid progression\u003c/p\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy ± immunotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eER: Estrogen receptor; PR: Progesterone receptor; HER2: Human Epidermal Growth Factor Receptor 2; TNBC: Triple-negative breast cancer.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*Ki-67 = a marker of cellular proliferation, used to distinguish Luminal A (low Ki-67) from Luminal B (high Ki-67)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e**CDK4/6 = cyclin-dependent kinases 4 and 6, targeted by specific inhibitors in HR+/HER2 − breast cancer to block cell cycle progression.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 | Study design\u003c/h2\u003e\n \u003cp\u003eAggregated, de-identified incidence data by clinical stage were obtained from the Belgian Cancer Registry (BCR, 2024), which compiles nationwide data from oncology programmes and pathology laboratories (age, stage, tumour and patient characteristics)(31) and exempted this study from ethical review. COVID-19–related diagnostic disruption was represented by 135 undiagnosed cancer (UDC) patients that should have been diagnosed in 2020 based on 2017–2019 predictions. Two subtype-specific Markov cohort models were developed, each simulating over five years the movement of two closed incident cohorts of 10,147 adult female breast cancer patients: a non-disrupted care cohort, estimated from 2017–2019 trends using a Poisson model, and a disrupted-care cohort reflecting observed 2020 cases (symptomatic, opportunistic, and resumed screening). Predicted stage-specific values were adjusted to match the observed decline, ensuring equal cohort sizes for unbiased comparison. UDC were derived by subtracting observed stage I–IV cases in 2020 from the corrected predictions.\u003c/p\u003e\n \u003cp\u003eAge stratification was not applied, as molecular subtype is the primary driver of prognosis and treatment decisions(16, 17). While age may influence care through comorbidities or patient preference, including it would have added complexity without improving model validity. Because subtype-specific incidence data were unavailable, pre-COVID Belgian subtype proportions were applied to both cohorts by multiplying each stage count by the corresponding subtype share to estimate per-subtype counts.\u003c/p\u003e\n \u003cp\u003eA five-year time horizon was chosen to capture the period of highest recurrence risk and cost concentration, particularly for aggressive subtypes such as TNBC and HER2+(28), and to align with follow-up guidelines and available evidence. A three-month cycle length was used, reflecting typical treatment intervals (e.g., chemotherapy cycles, clinical follow-ups) (16). The model adopted a Belgian healthcare payer perspective, focusing on direct medical costs (treatments, consultations), as this reflects the National Institute for Health and Disability Insurance (NIHDI) reimbursement practice and avoids uncertainty associated with missing subtype-specific data on productivity losses.\u003c/p\u003e\n \u003cp\u003eThe model was developed in Excel, with patients distributed at baseline (cycle 0) across the initial health states—‘Stage I Diagnosis \u0026amp;Treatment (D\u0026amp;T)’, ‘Stage II D\u0026amp;T’, ‘Stage III D\u0026amp;T’, ‘Stage IV D\u0026amp;T’, and ‘UDC’ (see Fig.\u0026nbsp;1)—according to BCR incidence data. Each cohort then progressed through health states via a transition matrix, which applied predefined probabilities to redistribute patients every three months. Each health state was associated with specific healthcare costs and utilities (see 2.4 Data inputs). By iterating these cycles, the simulation generated a dynamic representation of the patient population’s trajectory over the five-year horizon. Ultimately, the model compared QALYs (where 1 = perfect health and 0 = death) and healthcare costs between cohorts. Input parameters for the simulation, including transition probabilities, healthcare costs, and utilities, are detailed in Table\u0026nbsp;4, with calculation methods provided in the Additional file 1.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBreast cancer cases by stage and subtype in non-disrupted (2017–2019) and disrupted (2020) cohorts (aggregated data from the Belgian Cancer Registry, 2024)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eNon-disrupted care cohort (Pre-COVID-19 situation, 2017–2019)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage (clinical) at diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2-positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriple-negative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 769 (47.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e620\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 904\u003c/p\u003e\n \u003cp\u003e(38.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e716\u003c/p\u003e\n \u003cp\u003e(7.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e758\u003c/p\u003e\n \u003cp\u003e(7.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisrupted-care cohort (COVID-19 situation, 2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage (clinical) at diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2-positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriple-negative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 551 (44.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 949\u003c/p\u003e\n \u003cp\u003e(38.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e753\u003c/p\u003e\n \u003cp\u003e(7.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e759\u003c/p\u003e\n \u003cp\u003e(7.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUndiagnosed Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003cp\u003e(1.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cem\u003eNote: Values represent aggregated patient counts and have been rounded; minor discrepancies may occur.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline input parameters for non-disrupted care (pre-COVID-19) and disrupted care (COVID-19) cohorts by molecular subtype\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eTransition probabilities\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInput parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBase Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOSA – 95% CI Lower Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOSA – 95% CI Upper Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSource(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003cp\u003eD \u0026amp; T to Dead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"12\"\u003e\n \u003cp\u003e(34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003cp\u003eD \u0026amp; T to Dead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003cp\u003eD \u0026amp; T to Dead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I-III FU to Stage I-III LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"12\"\u003e\n \u003cp\u003e(28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I-III FU to Stage IV D \u0026amp; 1L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I-III FU to Dead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I-III LRR to Dead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e(35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003cp\u003eD \u0026amp; 1L to Stage IV 2L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"28\"\u003e\n \u003cp\u003e(36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003cp\u003eD \u0026amp; 1L to Dead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV 2L to Stage IV 3L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV 2L to Dead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV 3L to Stage IV 4L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV 3L to Dead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV 4L to Dead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUDC to Stage I\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eD \u0026amp; T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"20\"\u003e\n \u003cp\u003eHypothesized based on clinical evidence of stage progression after diagnostic delay (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUDC to Stage II\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eD \u0026amp; T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUDC to Stage III\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eD \u0026amp; T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUDC to Stage IV\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eD \u0026amp; 1L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUDC to Dead\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eUtilities\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInput parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBase Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisutility\u003c/p\u003e\n \u003cp\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOSA – 95% CI Lower Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOSA – 95% CI Upper Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSource(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e(37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003cp\u003eD \u0026amp; T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e(38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(39–41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(41, 42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003cp\u003eD \u0026amp; T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e(38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(39–41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(41, 42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003cp\u003eD \u0026amp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e(38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(39–41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(41, 42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I-III FU (adjuvant therapy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e(38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I-III FU (post-adjuvant therapy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e(38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I-III LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e(43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003cp\u003eD \u0026amp; T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e(43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV 2L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e(45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV 3L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e(46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV 4L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e(50, 51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eDirect healthcare costs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInput parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBase Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOSA – 95% CI Lower Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOSA – 95% CI Upper Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSource(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003cp\u003eD \u0026amp; T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3 657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"12\"\u003e\n \u003cp\u003eNomenSoft nomenclature codes\u003c/p\u003e\n \u003cp\u003e(see the Additional file 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3 657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 440\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6 081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e22 795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003cp\u003eD \u0026amp; T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3 657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 440\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3 657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 440\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10 131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e22 795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003cp\u003eD \u0026amp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10 915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23 579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I-III FU (adjuvant therapy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eBCPI(54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12 948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19 139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I-III FU (post-adjuvant therapy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eNomenSoft nomenclature codes\u003c/p\u003e\n \u003cp\u003e(see the Additional file 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I-III LRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3 918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eMean Stage I-III D\u0026amp;T costs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3 918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9 042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23 057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003cp\u003eD \u0026amp; T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"16\"\u003e\n \u003cp\u003eBCPI(54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13 173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19 818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 480\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV 2L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e11 688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e20 131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV 3L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23 297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 655\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23 297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 655\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3 833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7 537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV 4L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1 014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1 014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15 809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1 001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eScenario Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInput parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBase Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScenario Analysis - Multiplier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOSA – 95% CI Lower Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOSA – 95% CI Upper Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSource(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUDC to Stage I\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eD \u0026amp; T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"20\"\u003e\n \u003cp\u003eHypothesized based on clinical evidence of stage progression after diagnostic delay (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUDC to Stage II\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eD \u0026amp; T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUDC to Stage III\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eD \u0026amp; T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUDC to Stage IV\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eD \u0026amp; 1L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUDC to Dead\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003eD: Diagnosis; FU: Follow-Up; LRR: Locoregional Recurrence; OSA: One-way Sensitivity Analysis; T: Treatment; TNBC: Triple-Negative Breast Cancer; UDC: Undiagnosed Cancer; 1L: 1st systemic therapy line; 2L: 2nd systemic therapy line; 3L: 3rd systemic therapy line; 4L: 4th systemic therapy line\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003e* Bold input parameters represent those only present during the disrupted care situation (COVID-19)\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003e** 95% CI: 95% confidence interval, which was calculated as follows: Base case value ± 1.96 x SE\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003e*** Standard errors were calculated using\u003c/em\u003e \\(\\:SE=SD/\\sqrt{n}\\) \u003cem\u003efor means and\u003c/em\u003e \\(\\:SE=/\\sqrt{p(1-p)/n}\\) \u003cem\u003efor proportions\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003e**** Hypothesized input values were underlined\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 | Markov Model structure\u003c/h2\u003e\n \u003cp\u003eThe model consisted of 11 health states (Fig.\u0026nbsp;1), grouped into non-metastatic (stages I–III) and metastatic (stage IV) disease. Non-metastatic disease begins with a 'diagnosis and treatment' (D\u0026amp;T) phase, which includes diagnostic work-up, neoadjuvant therapy (for HER2 + and TNBC), surgery (with reconstruction if applicable), and radiotherapy. This phase is represented in the model as 'Stage I D\u0026amp;T,' 'Stage II D\u0026amp;T,' or 'Stage III D\u0026amp;T,' depending on the initial stage at diagnosis. Patients remain in the D\u0026amp;T state for one or more cycles depending on their subtype-specific treatment duration (Table\u0026nbsp;3), before transitioning to 'Stage I–III Follow-Up (FU)' for a defined period of adjuvant therapy (AT). We modelled AT as five years (20 cycles) for luminal A and seven years (28 cycles) for luminal B, reflecting standard practice for hormone receptor–positive disease and extended therapy increasingly recommended for luminal B due to its higher risk of late recurrence(32).\u003c/p\u003e\n \u003cp\u003eGiven the differing nature and duration of AT across molecular subtypes, a distinct ‘Stage I–III FU’ health state was defined. After AT, patients enter this state with follow-up every three months until recurrence. Some, however, may not achieve a disease-free period and progress directly to recurrence. In both cases, we simplify this by saying that patients move to ‘Stage I–III Locoregional Recurrence (LRR)’ if the evolution is locoregional or to ‘Stage IV D\u0026amp;T’ if metastatic. Those in ‘Stage I–III LRR’ receive treatment during one cycle and then return to follow-up, while patients entering the metastatic pathway (Stage IV states) start systemic therapy in ‘Stage IV D\u0026amp;T’ and transition through subsequent lines of treatment as disease advances.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDuration spent in initial treatment-related health states by molecular subtype, independent of COVID-19 disruptions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHealth States\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTriple-Negative\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I D \u0026amp; T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 months\u003c/p\u003e\n \u003cp\u003e(1 cycle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 months\u003c/p\u003e\n \u003cp\u003e(1 cycle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 months\u003c/p\u003e\n \u003cp\u003e(3 cycles)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 months\u003c/p\u003e\n \u003cp\u003e(3 cycles)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage II D \u0026amp; T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 months\u003c/p\u003e\n \u003cp\u003e(1 cycle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 months\u003c/p\u003e\n \u003cp\u003e(1 cycle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 months\u003c/p\u003e\n \u003cp\u003e(3 cycles)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 months\u003c/p\u003e\n \u003cp\u003e(3 cycles)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage III D \u0026amp; T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 months\u003c/p\u003e\n \u003cp\u003e(1 cycle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 months\u003c/p\u003e\n \u003cp\u003e(1 cycle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 months\u003c/p\u003e\n \u003cp\u003e(3 cycles)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 months\u003c/p\u003e\n \u003cp\u003e(3 cycles)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I-III FU (Adjuvant therapy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 years\u003c/p\u003e\n \u003cp\u003e(20 cycles)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 years\u003c/p\u003e\n \u003cp\u003e(28 cycles)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003cp\u003e(4 cycles)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003cp\u003e(4 cycles)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eD\u0026amp;T: diagnosis and treatment; FU: follow-up\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: Stage IV (metastatic) disease is not included in this table, as it follows a distinct clinical pathway characterized by successive lines of systemic therapy rather than structured, stage-based treatment and follow-up. The durations listed here reflect initial, curative-intent treatment for non-metastatic disease (stages I–III) prior to progression.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003ePatients diagnosed de novo with metastatic disease also start at ‘Stage IV D\u0026amp;T’, where they receive a first line of systemic therapy. They remain in this state (illustrated through the looping arrow) until the previous treatment lose effectiveness, or in case of progression (when the disease never stabilized), directly transition to the second-line regimen. This process continues sequentially up to four lines of metastatic treatment: ‘Stage IV 2L’, ‘3L’, and ‘4L’. A separate health state, ‘Undiagnosed Cancer’ (UDC), captures patients whose diagnosis was delayed due to COVID-19-related disruptions and who can subsequently transition to any stage at diagnosis once detected. During any cycle, patients in any health state can transition to ‘Dead’ (absorbing state).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eD \u0026amp; T: Diagnosis and treatment; FU: Follow-up; L: Lines of systemic therapy; LRR: Locoregional recurrence\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eL: Lines of systemic therapy; UDC : Undiagnosed cancer\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e↺ = Probability of remaining in the state\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e→ = Probability of moving to another state\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e] = Probability for patients in any state to move to the dead state\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 | Model validation\u003c/h2\u003e\n \u003cp\u003eClinical validation was conducted through consultation rounds with oncologists C.M. and E.N., to validate the model structure (i.e., model diagram), input parameters (i.e., healthcare costs), and key assumptions (i.e., simplified treatment pathways), ensuring alignment with current clinical practice and expert opinion. Additionally, five‑ and ten‑year mortality outcomes from the non-disrupted care model were validated against published subtype‑specific survival rates(33), converted to mortality values (mortality = 1 – survival), which were consistent with our estimates.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 | Data inputs\u003c/h2\u003e\n \u003cp\u003eTo operationalise our Markov model, aggregated incidence data from the BCR were used, transition probabilities and utilities were extracted from the literature, and healthcare costs were sourced from the Belgian medical nomenclature database (NomenSoft) and the Belgian pharmacotherapeutic information database (BCPI). All input values were stratified by health state and molecular subtype. The selected input values are presented in Table\u0026nbsp;4, with calculation methods and rationale detailed in the Additional file 1.\u003c/p\u003e\n \u003cp\u003eBoth models used the same input values, except for aggregated incidence data and transition probabilities from the UDC state to stage I–IV D\u0026amp;T. In the non-disrupted care model, UDC cases were absent and no such transitions occurred, as no care disruptions took place. In contrast, the disrupted care model included UDC cases with hypothesized transition probabilities to stage I–IV D\u0026amp;T to reflect diagnostic delays.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInsert\u003c/strong\u003e Table 4 \u003cstrong\u003ehere\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.4.1 | Epidemiological data\u003c/h2\u003e\n \u003cp\u003eAs mentioned above, stage-specific incidence data for breast cancer diagnoses for the disrupted and non-disrupted care cohorts were obtained from the BCR. Under 5% of diagnoses in the BCR dataset lacked definitive staging (unknown stage). Reasons for incomplete staging differed between non-disrupted care cohort (e.g., frail elderly) and disrupted care cohort (e.g., potentially, interrupted diagnostics due to intensive care unit admission). Given the uncertainty in distribution, the minimal overall percentage, and the absence of a statistically significant difference between cohorts, unknown stage cases were excluded from the main model to ensure consistent comparisons.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.4.2 | Transition probabilities\u003c/h2\u003e\n \u003cp\u003eThe 3-month subtype-specific transition probabilities were mainly derived from international retrospective cohort studies (Table\u0026nbsp;4). We hypothesized 3-month transition probabilities from UDC to ‘Stage I–IV D\u0026amp;T’ and ‘Dead’ for each molecular subtypes—given the absence of subtype-specific incidence data on UDC—based on a study which showed that aggressive subtypes (i.e., TNBC and HER2+) reach stage progression much earlier than luminal-like cancers (26).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e2.4.5 | Healthcare costs\u003c/h2\u003e\n \u003cp\u003eHealthcare costs for each health state were estimated from the perspective of the Belgian healthcare payer (NIHDI). The model's costs represent the official reimbursed amounts (nomenclature codes 1300/1600), which excludes patient co-payments (codes 3300/3600) to reflect only the payer's expenditure. Diagnostic, surgical, reconstruction, and radiotherapy costs were retrieved from NomenSoft(55), which provides official reimbursement rates (codes are available in the Additional file 1). Subtype-specific drug costs for neoadjuvant and AT were obtained from the Belgian Centre for Pharmacotherapeutic Information (BCPI)(54), with dosages and schedules based on phase III trials and validated by two oncologists (C.M., E.N.). All reimbursement values were indexed to 2020 prices using the Belgian health index (56) to ensure consistency with the model’s reference year.\u003c/p\u003e\n \u003cp\u003eSubtype-specific treatment pathways (Table\u0026nbsp;5) were simplified to balance between typical clinical practice and ensure feasibility within the Markov framework, and were based on ESMO clinical practice guidelines(16, 17), which underpin Belgian national recommendations. Some treatment regimens were generalized, and treatment durations were averaged across patient groups. A standardized diagnostic pathway was assumed across all molecular subtypes, including biopsy, staging imaging, blood analysis, and mammography. For simplicity, we assumed only patients with HER2 + and TNBC to have subtype-specific neoadjuvant therapy. Surgery was stratified by stage: All stage I-II patients underwent breast-conserving surgery (BCS) without reconstruction, while for stage III patients, 70% underwent BCS and 30% underwent mastectomy, with 25% assumed to receive reconstruction. All patients received the standard hypofractionated whole-breast radiotherapy regimen of 15 fractions, and related consultations before, during, and after treatment. AT also differed based on the molecular subtype. Post-adjuvant therapy follow-up included annual mammography, ultrasonography, and four consultations per year. For stage I-III patients developing LRR, healthcare costs were assumed to mirror those of initial D\u0026amp;T, calculated as the mean cost across stages I-III. Metastatic disease treatments followed subtype-specific systemic therapy lines, with healthcare costs also estimated on a 3-month cycle basis for comparability across subtypes. For simplicity, luminal B was assumed to have the same costs as luminal A.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSimplified treatment pathway across health states and molecular subtype\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHealth state\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLuminal-like\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage I D \u0026amp; T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003cp\u003e+ BCS\u003c/p\u003e\n \u003cp\u003e+ RT (+ pre, per, post consultations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003cp\u003e+ Neoadjuvant therapy\u003c/p\u003e\n \u003cp\u003e+ BCS\u003c/p\u003e\n \u003cp\u003e+ RT (+ pre, per, post consultations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003cp\u003e+ Neoadjuvant therapy\u003c/p\u003e\n \u003cp\u003e+ BCS\u003c/p\u003e\n \u003cp\u003e+ RT (+ pre, per, post consultations)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage II D \u0026amp; T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003cp\u003e+ BCS\u003c/p\u003e\n \u003cp\u003e+ RT (+ pre, per, post consultations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003cp\u003e+ Neoadjuvant therapy\u003c/p\u003e\n \u003cp\u003e+ BCS\u003c/p\u003e\n \u003cp\u003e+ RT (+ pre, per, post consultations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003cp\u003e+ Neoadjuvant therapy\u003c/p\u003e\n \u003cp\u003e+ BCS\u003c/p\u003e\n \u003cp\u003e+ RT (+ pre, per, post consultations)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage III D \u0026amp; T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003cp\u003e+ 70% BCS and 30% MST\u003c/p\u003e\n \u003cp\u003e(+ 25% reconstruction)\u003c/p\u003e\n \u003cp\u003e+ RT (including pre, per, post consultations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003cp\u003e+ Neoadjuvant therapy\u003c/p\u003e\n \u003cp\u003e+ 70% BCS and 30% MST (+ 25% reconstruction)\u003c/p\u003e\n \u003cp\u003e+ RT (including pre, per, post consultations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003cp\u003e+ Neoadjuvant therapy\u003c/p\u003e\n \u003cp\u003e+ 70% BCS and 30% MST (+ 25% reconstruction)\u003c/p\u003e\n \u003cp\u003e+ RT (including pre, per, post consultations)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage I-III FU (Adjuvant therapy)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjuvant endocrine therapy:\u003c/p\u003e\n \u003cp\u003eEither: tamoxifen/letrozole/\u003c/p\u003e\n \u003cp\u003eExemestane/ anastrozole\u003c/p\u003e\n \u003cp\u003e+ triptorelin/goserelin (if premenopausal women)\u003c/p\u003e\n \u003cp\u003e+ CDK4 inhibitors: Abemaciclib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003cp\u003e+Anti-HER2 therapy\u003c/p\u003e\n \u003cp\u003e(+ Endocrine therapy if HER2+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmunotherapy and Chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage I-III FU\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Post-adjuvant therapy)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMammography\u003c/p\u003e\n \u003cp\u003eUltrasonography\u003c/p\u003e\n \u003cp\u003e4 consultations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMammography\u003c/p\u003e\n \u003cp\u003eUltrasonography\u003c/p\u003e\n \u003cp\u003e4 consultations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMammography\u003c/p\u003e\n \u003cp\u003eUltrasonography\u003c/p\u003e\n \u003cp\u003e4 consultations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage I-III LRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSame as ‘Stage I-III D\u0026amp;T’\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSame as ‘Stage I-III D\u0026amp;T’\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSame as ‘Stage I-III D\u0026amp;T’\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage IV D \u0026amp; T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosis + endocrine therapy and CDK4/6 inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-HER2 and Chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmunotherapy and Chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage IV 2L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTargeted therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-HER2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTargeted therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage IV 3L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy for patients with endocrine resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy and Anti-HER2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage IV 4L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy for patients with endocrine resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy and Anti-HER2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003eBCS: breast-conserving surgery; CDK4/6 inhibitors: cyclin-dependent kinases 4 and 6; D\u0026amp;T: Diagnosis and treatment; FU: Follow-up; HER2: Human epidermal growth factor receptor 2–positive; LRR: Locoregional recurrence; MST: Mastectomy; RT: Radiotherapy\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e2.4.6 | Utilities\u003c/h2\u003e\n \u003cp\u003eHealth-state utilities were derived from EQ-5D-5L data(37, 38, 43) using a two-step approach. Baseline utilities for Belgian women of all ages were taken from the national Health Interview Survey (37). Health-state-specific disutilities from international studies (38, 43) were then subtracted, assuming comparable disease impacts across similar countries (e.g., the Netherlands). When disutilities were unavailable, values were estimated based on literature(39–42, 44–49, 51–53, 57–60), considering subtype-specific treatment burden, disease progression, and HRQoL changes. Utilities for luminal B were assumed to be equal to those of luminal A. Utilities for the UDC reflected general population levels, as asymptomatic patients were undiagnosed and likely unaffected by disease-specific HRQoL.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e2.6 | Analysis\u003c/h2\u003e\n \u003cp\u003eBy running both models, incremental differences in QALYs and healthcare costs between the disrupted care and non-disrupted care situations were estimated across subtypes over a five-year time horizon. QALYs were estimated by multiplying the utility value of each health state by the number of patients in that state per cycle and the cycle length. Summing these values across all cycles yielded the total QALYs. Healthcare costs were calculated similarly, by applying cost estimates to each state and aggregating them over all cycles.\u003c/p\u003e\n \u003cp\u003eTo reflect present-day valuations, both healthcare costs and QALYs were discounted annually according to Belgian guidelines—3.0% for costs and 1.5% for health effects (27). Additionally, the models were used to calculate the percentage point differences in mortality and the estimated number of additional deaths attributable to delayed diagnosis across molecular subtypes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e2.7 | Scenario and sensitivity analyses\u003c/h2\u003e\n \u003cp\u003eA one-way sensitivity analysis (OSA) and a probabilistic sensitivity analysis (PSA) were performed to assess parameter uncertainty and evaluate the robustness of results across each molecular subtype. The OSA included transition probabilities, utilities, and direct medical costs. Each parameter was varied individually across a range defined by its 95% confidence interval, calculated as the base-case value ± 1.96 × standard error (SE), while holding all other parameters constant. Two tornado diagrams were produced: one showing the impact of varying transition probabilities and utilities on incremental QALYs, and another showing the impact of varying transition probabilities and treatment costs on incremental healthcare costs. Both diagrams reflect results combined across all molecular subtypes.\u003c/p\u003e\n \u003cp\u003eFor the PSA, beta distributions were applied to probabilities and utilities, and gamma distributions to costs. Distribution parameters were derived from base-case values and their SEs, calculated from reported sample sizes where available. For parameters without reported variance, plausible 95% ranges (for probabilities/utilities) or an assumed coefficient of variation of 0.10 (for costs) were used to reflect uncertainty. The PSA was conducted using Monte Carlo simulation with 1,000 iterations in Visual Basic for Applications (VBA) within Excel.\u003c/p\u003e\n \u003cp\u003eThree scenario analyses were conducted to assess structural and methodological assumptions. A 10-year time horizon was used to explore longer-term outcomes, particularly early recurrence peaks in HER2 + and TNBC (around two years) and the slower recurrence pattern of luminal A and B (beyond 10 years) (28, 61, 62). The same utility and cost inputs per cycle were maintained, with adapted transition probabilities reflecting recurrence risks beyond five years (28). In the second scenario, we adjusted the base-case 3-month transition probabilities from the UDC state to “Stage I–IV D\u0026amp;T” for each molecular subtype. The base case assumed slower-growing subtypes (e.g., luminal A) were more often diagnosed at early stages, and aggressive subtypes (e.g., TNBC) at later stages, based on clinical evidence(13, 26). This scenario applied subtype-specific multipliers (Table\u0026nbsp;4) to model more severe progression, increasing late-stage and reducing early-stage diagnoses. As HER2 + and TNBC already had high late-stage probabilities, adjustments were minor, whereas larger multipliers were applied to luminal-like subtypes to test possible underestimation of progression severity. The third scenario examined the impact of changes in radiotherapy reimbursement tariffs during the COVID-19 pandemic. As part of emergency response strategies, several Belgian hospitals adopted hypofractionated radiotherapy (five fractions instead of 15) to reduce hospital visits, minimise infection risks, and optimise resource use(63). This protocol was reimbursed under a higher-cost category (€2,581.73 - nomenclature code: 444710–444721) rather than the standard category III (€1,999.67 in 2020 - nomenclature code: 444150–444161). Belgian data indicate that 8.2% of patients received five fractions in 2020(15). Exploring this scenario is relevant to capture the potential cost implications of shifts in clinical practice and reimbursement during the pandemic.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 | Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1 | Base case analysis\u003c/h2\u003e\u003cp\u003eThe Markov model estimated that COVID-19 related diagnostic delays in breast cancer across Belgium from March-June 2020, led to a total loss of 20.7 QALYs and \u0026euro;3.19\u0026nbsp;million in additional healthcare costs over five years, compared to a situation without disruption (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This corresponds to an average loss of \u0026euro;315 per patient and 0.002 QALYs per patient\u0026mdash;representing less than one day of life in full health (far below the minimal clinically important difference (MCID) typically cited for oncology, around 0.03 QALYs per year per patient(\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e)). Deterministic model outputs are point estimates derived from fixed parameter values and do not yield confidence intervals; uncertainty in these estimates was instead explored through the PSA.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBase case: Five-year QALY and cost changes by breast cancer subtype under care disruption.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular\u003c/p\u003e\u003cp\u003eSubtype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCohort\u003c/p\u003e\u003cp\u003e(n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eNon-disrupted care (2017\u0026ndash;2019)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eDisrupted-care (2020)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u003cp\u003eIncremental costs \u0026amp; QALYs\u003c/p\u003e\u003cp\u003e(Disrupted-care vs. Non-disrupted care)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCost (\u0026euro;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDeath %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCost (\u0026euro;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDeath %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e∆QALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e∆Cost (\u0026euro;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e∆% Point\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eAdditional deaths\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eLuminal A\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal cohort\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5 479)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 987.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e179 522 108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20 982.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e180 004 052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-4.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e481 944\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32 853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eLuminal B\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal cohort\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2 740)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 378.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103 245 734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10 375.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e103 770 423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-4.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e524 689\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37 681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e37 872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e191\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eHER2+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal cohort\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;609)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 242.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 155 748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e8.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 240.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e82 702 749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e8.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-2.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e547 000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e134 903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e135 801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e898\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eTNBC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal cohort\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1 319)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 764.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e319 433 158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e10.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 754.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e321 072 481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e11.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-9.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e1 639 323\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e242 178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e243 421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e1 243\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eBreast cancer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal cohort\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10 147)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" morerows=\"1\" nameend=\"c8\" namest=\"c3\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-20.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e3 192 956\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c12\" namest=\"c11\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e315\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003eHER2+: Human Epidermal Growth Factor Receptor 2\u0026ndash;positive; QALY: Quality-Adjusted Life Year; TNBC: Triple-Negative Breast Cancer\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAmong molecular subtypes, TNBC accounted for the largest QALY loss (-9.5), followed by luminal A (-4.8), luminal B (-3.9), and HER2+ (-2.5). TNBC patients also incurred the highest additional direct healthcare costs (\u0026euro;1.64\u0026nbsp;million), followed by HER2+ (\u0026euro;547,000), luminal B (\u0026euro;524,689), and luminal A (\u0026euro;481,944). Per patient, the largest QALY loss was observed in TNBC (-0.007 QALYs, or 2.56 days of full health), followed by HER2+ (-0.004 QALYs, or 1.46 days of full health), and the luminal-like subtypes, both below one day of full health (-0.001 QALYs). TNBC patients incurred the highest additional per-patient costs (\u0026euro;1,243), followed by HER2+ (\u0026euro;898), luminal B (\u0026euro;191), and luminal A (\u0026euro;88).\u003c/p\u003e\u003cp\u003eDelayed cancer diagnoses resulted in a slight increase in mortality across all subtypes. The largest absolute increase in mortality occurred in TNBC (+\u0026thinsp;0.20 percentage points), resulting in two additional deaths. Luminal A and B subtypes showed increases in mortality of 0.08 and 0.05 percentage points, corresponding to two and one additional death(s), respectively. HER2\u0026thinsp;+\u0026thinsp;increased by 0.07 percentage points, also leading to one additional death. For TNBC, this corresponds to a 1.9% relative increase compared to non-disrupted care. Luminal A and B showed relative increases of 2.9% and 1.2%, respectively, while HER2\u0026thinsp;+\u0026thinsp;increased by 0.9%. Relative changes in mortality were calculated as the percentage difference from the non-disrupted care situation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 | One-Way Sensitivity Analysis (OSA)\u003c/h2\u003e\u003cp\u003eThis section presents the OSA results examining how variations in transition probabilities, costs, and utilities influence both changes in incremental QALYs and healthcare costs. Two tornado diagrams (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) are provided to illustrate the parameters driving the greatest changes.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 | The impact of varying transition probabilities and utilities on incremental QALYs\u003c/h2\u003e\u003cp\u003eThe OSA on QALYs showed that the model was most sensitive to changes in the utilities for TNBC. Particularly, utilities for stage I D\u0026amp;T, stage I\u0026ndash;III adjuvant therapy, stage IV D\u0026amp;T, and stage II D\u0026amp;T had the largest impact on incremental QALYs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Tornado diagram showing the impact of varying transition probabilities and utilities on incremental QALYs across breast cancer molecular subtypes\u003c/p\u003e\u003cp\u003e\u003cem\u003eL: Lines of systemic therapy (2L: second therapy line; 3L: third therapy line; 4L: fourth therapy line); LRR: Locoregional recurrence; LUM A: Luminal A; LUMA B: Luminal B; QALY: Quality-Adjusted Life Year; TNBC: Triple-negative breast cancer; TP: Transition probabilities; UDC: Undiagnosed cancer\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e*The vertical dashed line represents the base case incremental QALY result. Dark grey bars indicate the change when the parameter is set to its lower bound, and light grey bars indicate the change when set to its upper bound.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 | The impact of varying model inputs on incremental healthcare cost changes\u003c/h2\u003e\u003cp\u003eThe OSA on healthcare costs showed that the model was most sensitive to cost variations in TNBC. Specifically, the costs of stage I D\u0026amp;T and stage IV D\u0026amp;T, which had the largest impacts on total incremental healthcare costs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Tornado diagram showing the impact of varying transition probabilities and treatment costs on incremental healthcare costs across breast cancer molecular subtypes.\u003c/p\u003e\u003cp\u003e\u003cem\u003eD\u0026amp;T: Diagnosis and treatment; L: Lines of systemic therapy (2L: second therapy line; 3L: third therapy line; 4L: fourth therapy line); LRR: Locoregional recurrence; LUM A: Luminal A; LUMA B: Luminal B; TNBC: Triple-negative breast cancer; TP: Transition probabilities; UDC: Undiagnosed cancer\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e*The vertical dashed line represents the base case incremental healthcare cost result. Dark grey bars indicate the change when the parameter is set to its lower bound, and light grey bars indicate the change when set to its upper bound.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4 | Scenario analyses\u003c/h2\u003e\u003cp\u003eExtending the time horizon from five to 10 years revealed a more pronounced impact of COVID-19-related disruptions on both health and economic outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). QALY losses widened from \u0026minus;\u0026thinsp;20.7 to -32.1, and incremental healthcare costs rose by \u0026euro;2.50\u0026nbsp;million (from \u0026euro;3.19\u0026nbsp;million to \u0026euro;5.69\u0026nbsp;million). Luminal-like subtypes experienced over a twofold increase in both QALY losses and healthcare costs, whereas increases were less pronounced for TNBC and HER2+: QALY losses doubled for HER2\u0026thinsp;+\u0026thinsp;but fell by more than half for TNBC, while costs rose by over 1.5-fold for both. Mortality trends were consistent with the base case, with TNBC showing the largest absolute increase (+\u0026thinsp;0.33 percentage points), followed by HER2+ (+\u0026thinsp;0.11), luminal B (+\u0026thinsp;0.06), and luminal A (+\u0026thinsp;0.04). These corresponded to relative increases in mortality of 1.6% for TNBC (from 20.44% to 20.77%), 0.8% for HER2+ (from 13.64% to 13.75%), 0.7% for luminal B (from 9.10% to 9.16%), and 0.6% for luminal A (from 6.36% to 6.40%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eScenario analysis: Incremental changes in QALYs and healthcare costs across three scenarios, by molecular subtype.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"14\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScenario type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMolecular Subtype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCohort\u003c/p\u003e\u003cp\u003e(n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eNon-disrupted care (2017\u0026ndash;2019)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003eDisrupted care (2020)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e\u003cp\u003eIncremental healthcare costs \u0026amp; QALYs\u003c/p\u003e\u003cp\u003e(Disrupted care vs. Non-disrupted care)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCost (\u0026euro;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDeath %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eQALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCost (\u0026euro;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eDeath%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e∆QALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e∆Cost (\u0026euro;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e∆% Point\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eAdditional deaths\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e10-year time horizon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLuminal A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 531.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e342 044 246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e6.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e39 517.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e343 230 607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e6.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1 186 360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62 428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e7.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e62 645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e217\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLuminal B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 182.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e228 963 281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e9.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e19 172.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e230 020 328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e9.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1 057 047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83 563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e6.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e83 949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHER2+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 103.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e128 324 067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e13.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e4 097.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e129 197 648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e13.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e873 581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e210 713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e6.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e212 147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1,434\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTNBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 538.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e431 548 773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e20.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e8 534.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e434 122 775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e20.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2 574 002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e327 179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e6.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e329 130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1 951\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBreast Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"7\" morerows=\"1\" nameend=\"c10\" namest=\"c4\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-32.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5 690 990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c14\" namest=\"c13\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e561\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eShift to advanced stages for the UDC\u003c/p\u003e\u003cp\u003epatients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLuminal A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 987.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e179 522 108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e20 972.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e184 012 139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e4 490 031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e33 585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLuminal B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 378.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e103 245 734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e10 371.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e105 063 870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1 818 135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37 681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38 344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e664\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHER2+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 242.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82 155 748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e8.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e2 239.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e82 838 914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e8.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e683 165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e134 903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e136 024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1 122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTNBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 764.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e319 433 158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e10.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e4 753.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e321 443 662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e11.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2 010 504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e242 178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e243 703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1 524\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBreast Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"7\" morerows=\"1\" nameend=\"c10\" namest=\"c4\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-36.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e9 001 835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c14\" namest=\"c13\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e887\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eShift in radiotherapy costs during COVID-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLuminal A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 987.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e179 522 108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e\u003cp\u003e2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20 982.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e180 740 027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1 217 920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e32 988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLuminal B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 378.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e103 245 734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e\u003cp\u003e4.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10 375.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e104 136 793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e891 059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37 681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38 006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHER2+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 242.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82 155 748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e\u003cp\u003e8.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2 240.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e82 856 543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e8.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e700 794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e134 903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e136 053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1 151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTNBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 764.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e319 433 158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e\u003cp\u003e10.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4 754.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e321 413 901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e11.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-9.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1 980 743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e242 178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e243 680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1 502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBreast\u003c/p\u003e\u003cp\u003eCancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"7\" morerows=\"1\" nameend=\"c10\" namest=\"c4\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-20.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e4 790 516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c14\" namest=\"c13\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer\u003c/p\u003e\u003cp\u003epatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e472\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003cem\u003eHER2+: Human Epidermal Growth Factor Receptor 2\u0026ndash;positive; QALY: Quality-Adjusted Life Year; TNBC: Triple-Negative Breast Cancer; UDC: Undiagnosed Cancer\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eApplying increased transition probabilities through multipliers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) from the UDC state towards advanced stage at diagnosis (i.e., stage III and IV) across subtypes resulted in nearly 37 QALYs lost and \u0026euro;9\u0026nbsp;million in additional healthcare costs, almost a threefold increase compared to the base case (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Luminal A and B subtypes incurred \u0026euro;4.49\u0026nbsp;million and \u0026euro;1.82\u0026nbsp;million in costs, and lost 15.17 and 7.70 QALYs, respectively. TNBC costs increased by \u0026euro;371,181 (to \u0026euro;2.01\u0026nbsp;million), and HER2\u0026thinsp;+\u0026thinsp;by \u0026euro;136,165 (to \u0026euro;683,165). This scenario projected 11 additional deaths\u0026mdash;almost twice the number in the base case\u0026mdash;mainly among luminal subtypes. Absolute mortality increases were highest in TNBC (+\u0026thinsp;0.22 percentage points), followed by luminal A and HER2+ (+\u0026thinsp;0.08 each) and luminal B (+\u0026thinsp;0.09). These corresponded to relative mortality increases of 2.0% for TNBC (from 10.81% to 11.03%), 2.9% for luminal A (from 2.80% to 2.88%), 2.2% for HER2+ (from 8.06% to 8.14%), and 2.2% for luminal B (from 4.04% to 4.13%).\u003c/p\u003e\u003cp\u003eWhen assuming that 8.2% of patients received a 5-fraction radiotherapy regimen(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u0026mdash;costlier than the standard 15-fraction course\u0026mdash;during the COVID-19 period, and that this practice persisted over five years, the model projected an increase of \u0026euro;1.60\u0026nbsp;million (to \u0026euro;4.79\u0026nbsp;million) in incremental breast cancer costs across all subtypes. This translated into a 2.5-fold increase for luminal A, 1.7-fold for luminal B, 1.3-fold for HER2+, and 1.2-fold for TNBC.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.5 | Probabilistic Sensitivity Analysis (PSA)\u003c/h2\u003e\u003cp\u003eScatterplots of the 1,000 PSA iterations were generated for each subtype to illustrate the distribution of incremental QALYs and healthcare costs (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the mean outcomes from the PSA by molecular subtype, including incremental QALYs and healthcare costs, as well as the corresponding 95% CIs.\u003c/p\u003e\u003cp\u003eAcross all molecular subtypes, the PSA revealed wide uncertainty in incremental QALYs and costs. For luminal A, the 95% CI for incremental QALYs ranged from \u0026minus;\u0026thinsp;1,255.36 to 1,249.99, with a 51% probability of QALY loss, while incremental healthcare costs ranged from -\u0026euro;26,790,493 to \u0026euro;24,962,257, with a 52% probability of cost increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For luminal B, QALY changes ranged from \u0026minus;\u0026thinsp;625.65 to 604.00 (49% probability of loss) and costs from -\u0026euro;19,523,679 to \u0026euro;19,107,800 (50% probability of increase) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For HER2+, QALYs ranged from \u0026minus;\u0026thinsp;160.11 to 154.10 (50% probability of loss) and costs from -\u0026euro;16,775,684 to \u0026euro;16,469,399 (52% probability of increase) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For TNBC, QALYs ranged from \u0026minus;\u0026thinsp;213.03 to 200.42 (54% probability of loss) and costs from -\u0026euro;47,871,357 to \u0026euro;53,229,055 (51% probability of increase) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These ranges illustrate that, while point estimates align with base-case findings (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the CIs encompass both potential gains and losses in QALYs as well as cost savings and increases.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eProbabilistic sensitivity analysis: QALY and cost changes by breast cancer subtype under care disruption.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular Subtype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreast cancer\u003c/p\u003e\u003cp\u003ecohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eNon-disrupted care (2017\u0026ndash;2019)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eDisrupted care (2020)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eIncremental costs \u0026amp; QALYs\u003c/p\u003e\u003cp\u003e(Disrupted care vs. non-disrupted care)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eQALYs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eCost (\u003c/b\u003e\u0026euro;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eQALYs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eCost (\u003c/b\u003e\u0026euro;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e∆QALYs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e∆Cost (\u003c/b\u003e\u0026euro;)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLuminal A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e95%\u003c/p\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 991.0\u003c/p\u003e\u003cp\u003e[20 962.2\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e21 019.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e179 335 236\u003c/p\u003e\u003cp\u003e[178 767 318\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e179,903 154)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 001.1\u003c/p\u003e\u003cp\u003e[20 972.8\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e21 029.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e179 752 285\u003c/p\u003e\u003cp\u003e[179 187 369\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e180 317 201]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-15.2\u003c/p\u003e\u003cp\u003e[-1 255.4\u0026ndash;1 249.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e417 049\u003c/p\u003e\u003cp\u003e[-26 790 493\u0026ndash;24 962 257]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLuminal B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e95%\u003c/p\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 375.6\u003c/p\u003e\u003cp\u003e[10 361.8\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e10 389.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103 543 022\u003c/p\u003e\u003cp\u003e[103 113 677\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e103 972 368]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 373.5\u003c/p\u003e\u003cp\u003e[10 360.0\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e10 386.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e103 641 533\u003c/p\u003e\u003cp\u003e[103 219 807\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e104 063 259]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003cp\u003e[-625.7\u0026ndash;604.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e98 511\u003c/p\u003e\u003cp\u003e[-19 523 679\u0026ndash;19 107 800]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHER2+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e95%\u003c/p\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 243.4\u003c/p\u003e\u003cp\u003e[2 240.2\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e2 246.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 244 443\u003c/p\u003e\u003cp\u003e[81 876 267\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e82 612 619]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 242.3\u003c/p\u003e\u003cp\u003e[2 238.9\u003c/p\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003cp\u003e2 245.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82 474 170\u003c/p\u003e\u003cp\u003e[82 126 409\u003c/p\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003cp\u003e82 812 930]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.0\u003c/p\u003e\u003cp\u003e[-160.1\u0026ndash;154.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e229 727\u003c/p\u003e\u003cp\u003e[-16 775 684\u0026ndash;16 469 399]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriple-Negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e95%\u003c/p\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 768.7\u003c/p\u003e\u003cp\u003e[4 762.5\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e4 775.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e319 960 341\u003c/p\u003e\u003cp\u003e[318 901 500\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e321 019 181]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 757.1\u003c/p\u003e\u003cp\u003e[4 754.1\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e4 760.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e321 551 351\u003c/p\u003e\u003cp\u003e[320 410 320\u003c/p\u003e\u003cp\u003e-\u003c/p\u003e \u003cp\u003e322 692 381]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-11.7\u003c/p\u003e\u003cp\u003e[-213.0\u0026ndash;200.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1 591 010\u003c/p\u003e\u003cp\u003e[-47 871 357\u0026ndash;53 229 055]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eHER2+: Human Epidermal Growth Factor Receptor 2\u0026ndash;positive; QALY: Quality-Adjusted Life Year; 95% CI: 95% confidence interval\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eQALY: Quality-Adjusted Life Year; 95% CI: 95% confidence interval\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003e\u003cem\u003eThe red solid line represents the mean value across simulations. Black dashed lines indicate the 95% CIs. The shaded grey area marks the CI range. Percentages refer to the proportion of simulations showing gains vs. losses (A) or cost increases vs. savings (B).\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eQALY: Quality-Adjusted Life Year; 95% CI: 95% confidence interval\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003e\u003cem\u003eThe red solid line represents the mean value across simulations. Black dashed lines indicate the 95% CIs. The shaded grey area marks the CI range. Percentages refer to the proportion of simulations showing gains vs. losses (A) or cost increases vs. savings (B).\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eQALY: Quality-Adjusted Life Year; 95% CI: 95% confidence interval\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003e\u003cem\u003eThe red solid line represents the mean value across simulations. Black dashed lines indicate the 95% CIs. The shaded grey area marks the CI range. Percentages refer to the proportion of simulations showing gains vs. losses (A) or cost increases vs. savings (B).\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eQALY: Quality-Adjusted Life Year; 95% CI: 95% confidence interval\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003e\u003cem\u003eThe red solid line represents the mean value across simulations. Black dashed lines indicate the 95% CIs. The shaded grey area marks the CI range. Percentages refer to the proportion of simulations showing gains vs. losses (A) or cost increases vs. savings (B).\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 | Discussion","content":"\u003cp\u003eThis modelling exercise projected that breast cancer diagnostic delays between March and June 2020 in Belgium had only a modest health and economic impact over five years. Among 10,147 patients, 135 undiagnosed cases were estimated to result in 21 QALYs lost (around 0.002 per patient, which is less than one day in full health and far below the MCID of 0.03 per patient per year (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e)), six additional deaths, and \u0026euro;3.2\u0026nbsp;million in extra healthcare costs (1% of Belgian annual breast cancer expenditure). From a public health perspective, the disruption\u0026rsquo;s impact was limited and smaller than expected, despite early reports and widespread concern(\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e) during the first pandemic months.\u003c/p\u003e\u003cp\u003eDiagnostic delays affect all breast cancers, but their impact depends on tumour biology: missing an aggressive subtype such as TNBC or HER2\u0026thinsp;+\u0026thinsp;carries greater risk than delaying diagnosis of luminal-like cancers(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In line with this rationale, our model applied clinically supported but hypothesised progression assumptions, which projected heterogeneity by subtype: TNBC and HER2\u0026thinsp;+\u0026thinsp;together accounted for nearly 70% of excess healthcare costs and 60% of QALY losses.\u003c/p\u003e\u003cp\u003eExtending the time horizon to 10 years yielded similarly limited impacts, with over 32 QALYs lost\u0026mdash;equivalent to around one day of full health per patient\u0026mdash;and incremental healthcare costs representing\u0026thinsp;\u0026lt;\u0026thinsp;2% (\u0026euro;5.7\u0026nbsp;million) of Belgian annual breast cancer expenditure. The relatively larger incremental QALY and cost impacts for luminal-like subtypes reflect their longer recurrence trajectories(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e), whereas most adverse outcomes for HER2\u0026thinsp;+\u0026thinsp;and TNBC, which recur earlier(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e), were already captured within the five-year horizon. Even when assuming higher probabilities of UDC being diagnosed at advanced stages across all subtypes, the projected 5-year impact remained modest, with 37 QALYs lost (under two days in full health per patient) and \u0026euro;9\u0026nbsp;million in incremental costs (\u0026lt;\u0026thinsp;3% of annual breast cancer expenditure).\u003c/p\u003e\u003cp\u003eAlthough the deterministic base-case analysis projected modest incremental effects, the PSA revealed that these estimates are highly uncertain. Across all molecular subtypes, the 95% CIs spanned both positive and negative values, with probabilities of QALY loss or cost increase close to 50%. This indicates that the base-case results represent only one possible outcome within a wide distribution: small changes in key parameters could plausibly result in either greater harm or no adverse effect at all. Much of this uncertainty stems from transition probabilities from UDC to stage-specific diagnosis and from utility estimates, particularly for TNBC and HER2\u0026thinsp;+\u0026thinsp;where data were limited. Consistent with this, the OSA identified TNBC inputs\u0026mdash;with especially wide 95% CI\u0026mdash;as having the greatest influence on overall results. The robustness of conclusions therefore hinges on the precision of such parameters, underscoring the need for higher-quality, subtype-specific data to reduce uncertainty.\u003c/p\u003e\u003cp\u003eThe limited incremental QALYs and healthcare costs are likely explained by the small proportion of UDC (1.33%) reported at the end of 2020, which were then modelled over a five-year period. Indeed, even though a larger proportion of patients were undiagnosed during the first few months of the pandemic(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), the majority was recovered by year-end. This is linked to Belgium\u0026rsquo;s screening programmes that resumed in June 2020 and \u0026lsquo;do-not-delay-care\u0026rsquo; campaigns(\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e), proactive primary-care outreach(\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e) and teleconsultation policies(\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e), which supported timely symptomatic presentation. Consistent with this, a Belgian observational study (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) found no evidence of a national stage shift, tumour size increase, or nodal progression in 2020, with DCIS diagnoses\u0026mdash;likely most of the missing cases\u0026mdash;rebounding by late 2020 and screening volumes recovering in 2021.\u003c/p\u003e\u003cp\u003eOur study is not the first to examine the potential health and economic consequences of COVID-19\u0026ndash;related diagnostic delays in breast cancer. Sud et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e) modelled delays in the UK\u0026rsquo;s 2-week-wait referral pathway applying per-day hazard ratios for treatment delay uniformly to all stage I\u0026ndash;III cases diagnosed through this route, which projected 733 excess breast cancer deaths and 15,339 life-years lost annually in England from a three-month delay. Maringe et al.,(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) using linked NHS registry and hospital data in a stage-shift model, estimated 281\u0026ndash;344 additional breast cancer deaths over five years in England due to pandemic-related diagnostic delays. Gheorghe et al.(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) then translated these mortality projections into 4,100 QALYs lost and \u0026pound;23.2\u0026nbsp;million in productivity losses. Degeling et al.(\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e), applying an Australian stage-shift model, projected 20\u0026ndash;64 life-years lost and AU\u003cspan\u003e$\u003c/span\u003e2.0\u0026ndash;7.7\u0026nbsp;million in extra costs for breast cancer under 3\u0026ndash;6 month delay scenarios.\u003c/p\u003e\u003cp\u003eDifferences between our findings and those of previous modelling studies can be explained by several factors. First, population sizes and healthcare systems differ between Belgium, the UK, and Australia, likely influencing the scale of the impact. Belgium\u0026rsquo;s smaller population, dense hospital network, and high acute bed capacity(\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e) supported resilience during the first wave, while rapid measures\u0026mdash;such as reimbursed teleconsultations(\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e), proactive primary care outreach, adapted cancer guidelines(\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e), and hypofractionated radiotherapy(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u0026mdash;helped sustain continuity of diagnosis and treatment. Importantly, breast cancer screening resumed in June 2020(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), earlier than in parts of the UK(\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e), which reduced the period of diagnostic delay. Coupled with strong inter-hospital collaboration(\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e) and national \u0026lsquo;do-not-delay-care\u0026rsquo; campaigns, these factors limited the proportion of undiagnosed cases that progressed to later stages. This helps explain why our model projected smaller QALY losses, lower additional healthcare costs, and fewer excess deaths compared with modelling studies from the UK and Australia, where screening interruptions persisted longer and system capacity was more constrained. Second, prior models varied substantially in design: Sud et al.(\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e) applied per-day hazards of delay across all stage I\u0026ndash;III cancers diagnosed via the 2-week-wait pathway; Maringe et al.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) modelled large shifts from routine or screening routes to emergency presentations; Gheorghe et al.(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) translated Maringe et al.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) excess deaths into QALY and productivity losses using a societal human-capital approach; and Degeling et al.(\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e) applied an inverse stage-shift method, conservatively modelling stage I to II progression under uniform 3\u0026ndash;6 month delays. By contrast, our subtype-specific Markov model compared two Belgian cohorts: the observed 2020 breast cancer population (with 1.33% UDC) and a projected \u0026lsquo;no disruption\u0026rsquo; cohort based on 2017\u0026ndash;2019 trends, with UDC progression under diagnostic delay hypothesised to stage-specific diagnoses according to molecular subtype. Third, the duration and intensity of disruption differed markedly: Belgian screening was suspended for approximately three months with rapid recovery, whereas UK scenarios assumed screening disruptions lasting up to 12 months(\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Finally, our analysis adopted a healthcare payer perspective, excluded DCIS and unknown-stage cases, and assumed constant treatment trajectories. These choices, combined with Belgium\u0026rsquo;s mitigation measures and the relatively rapid resumption of screening(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u0026mdash;in contrast to Scotland(\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e) and Wales(\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e) for instance, where screening remained suspended until August 2020\u0026mdash;may help explain the smaller health and economic impacts observed.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.1 | Strengths and limitations\u003c/h2\u003e\u003cp\u003eThe primary strength of this study lies in its use of a Markov model that integrates Belgian observational data on breast cancer incidence by clinical stage. To our knowledge, this is the first subtype-specific Markov model assessing the health and economic impact of COVID-19\u0026ndash;related breast cancer diagnostic delays in Belgium. Despite uncertainties in input parameters, robust sensitivity analyses and expert consultations strengthened the model\u0026rsquo;s credibility and ensured that limitations were explicitly addressed. The framework is adaptable to other countries, providing a versatile policy tool (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Several limitations should be noted. While stage-specific projections from the BCR enabled estimation of UDC by stage in 2020, data were not subtype-specific. Applying the overall stage distribution uniformly across subtypes may overestimate the contribution of aggressive cancers, which are less likely to remain undiagnosed, while underestimating their impact, as missed cases would more plausibly present at later stages. A related limitation is that the model assumed equal probability of missed diagnoses across stages, although most undiagnosed cases were likely early-stage, screen-detected tumours. Some input parameters\u0026mdash;such as transition probabilities from UDC to stage-specific diagnosis by subtype and selected disutilities (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u0026mdash;were not empirically available and were instead specified as plausible assumptions informed by clinical evidence. While this approach enabled model implementation, it inevitably introduces additional uncertainty in the incremental outcomes, as reflected in the PSA. Simplifying treatment trajectories (e.g., equating costs and utilities between luminal subtypes) may have attenuated subgroup-specific differences, producing more moderate aggregate results than would be expected if full clinical heterogeneity were incorporated. While age is a key prognostic factor and BCR data are available by age group, the absence of stratification by subtype prevented the model from accounting for age-related differences in progression, treatment response, or survival within subtypes\u0026mdash;for instance, potentially larger QALY and healthcare cost impacts among younger women with aggressive cancers, and smaller impacts among older women due to higher competing mortality and less intensive treatment. The model assumed identical input parameters for disrupted and non-disrupted care, except for UDC and related transition probabilities. A third scenario incorporating Belgian data on radiotherapy reimbursement(\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e) (5-fraction vs. 15-fraction) showed that altering a single parameter could change incremental costs. Other adaptations\u0026mdash;telemedicine(\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e), early discharge(\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e), and organizational changes\u0026mdash;also likely affected costs and outcomes in both directions, so cumulative unmodelled effects may have differed from our estimates. Finally, certain clinical aspects were not modelled to avoid added complexity\u0026mdash;for example, recurrence risks vary by stage and subtype, but LRR within stages I\u0026ndash;III was not modelled separately.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.2 | Policy implications and future research\u003c/h2\u003e\u003cp\u003eAlthough concerns during the initial pandemic suggested severe consequences, our analysis and BCR data indicate that the early disruption of breast cancer diagnosis had a modest five-year impact that, while growing slightly when projected over a 10-year horizon, remained limited overall. This likely reflects the rapid recovery of screening, proactive GP outreach, and targeted \u0026ldquo;do-not-delay-care\u0026rdquo; campaigns, which helped avoid a sustained stage shift. For policy, these findings highlight the need to protect continuity of diagnostic services during system shocks, minimise backlogs once services resume, and ensure vigilance for aggressive tumours such as TNBC and HER2\u0026thinsp;+\u0026thinsp;by providing subtype-specific incidence data.\u003c/p\u003e\u003cp\u003eFuture work should build on these insights by maintaining registry-based monitoring to capture possible longer-term impacts, particularly in aggressive subtypes, and by incorporating outcomes such as recurrence, overall survival, and HRQoL. An important next step will be for cancer registries to routinely provide subtype-specific incidence and stage data, enabling more granular assessment of differential impacts and supporting robust, policy-relevant modelling. It will also be important to evaluate the cumulative impact of multiple pandemic waves and to assess the effects of treatment adaptations (e.g., telemedicine, altered radiotherapy regimens, early discharge) that may have influenced QALY and healthcare cost changes. Finally, this study illustrates the value of rapid modelling exercises as a complement to slower-to-emerge observational data, providing policymakers with timely evidence to guide mitigation strategies and strengthen preparedness for future health system disruptions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.3 | Conclusion\u003c/h2\u003e\u003cp\u003eThis study shows that breast cancer diagnostic delays during Belgium\u0026rsquo;s first COVID-19 wave had modest five-year consequences: 21 QALYs lost, six additional deaths, and \u0026euro;3.2\u0026nbsp;million in costs (\u0026lt;\u0026thinsp;1% of annual expenditure). These smaller-than-expected impacts are reassuring and likely reflect Belgium\u0026rsquo;s rapid screening recovery and mitigation measures, which prevented a sustained stage shift. Subtype-specific modelling, however, revealed that aggressive cancers such as TNBC and HER2\u0026thinsp;+\u0026thinsp;are more delay-sensitive than luminal-like subtypes. As projections were strongly assumption-driven and highly sensitive to uncertain inputs, higher-quality, subtype-specific data are needed. Ongoing monitoring of recurrence, survival, and HRQoL remains crucial to capture longer-term effects and guide policies that ensure diagnostic continuity and prioritise high-risk subtypes during future disruptions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAT: Adjuvant Therapy\u003c/p\u003e\n\u003cp\u003eBCPI: Belgian Centre for Pharmacotherapeutic Information\u003c/p\u003e\n\u003cp\u003eBCR: Belgian Cancer Registry\u003c/p\u003e\n\u003cp\u003eBCS: Breast-Conserving Surgery\u003c/p\u003e\n\u003cp\u003eCDK4/6: Cyclin-Dependent Kinases 4 and 6\u003c/p\u003e\n\u003cp\u003eCOVID-19: Coronavirus Disease 2019\u003c/p\u003e\n\u003cp\u003eD\u0026amp;T: Diagnosis and Treatment\u003c/p\u003e\n\u003cp\u003eDCIS: Ductal Carcinoma In Situ\u003c/p\u003e\n\u003cp\u003eER: Estrogen Receptor\u003c/p\u003e\n\u003cp\u003eESMO: European Society for Medical Oncology\u003c/p\u003e\n\u003cp\u003eFU: Follow-Up\u003c/p\u003e\n\u003cp\u003eHER2: Human Epidermal Growth Factor Receptor 2\u003c/p\u003e\n\u003cp\u003eHRQoL: Health-Related Quality of Life\u003c/p\u003e\n\u003cp\u003eLRR: Locoregional Recurrence\u003c/p\u003e\n\u003cp\u003eMCID: Minimal Clinically Important Difference\u003c/p\u003e\n\u003cp\u003eMST: Mastectomy\u003c/p\u003e\n\u003cp\u003eNIHDI: National Institute for Health and Disability Insurance\u003c/p\u003e\n\u003cp\u003eOSA: One-Way Sensitivity Analysis\u003c/p\u003e\n\u003cp\u003ePR: Progesterone Receptor\u003c/p\u003e\n\u003cp\u003ePSA: Probabilistic Sensitivity Analysis\u003c/p\u003e\n\u003cp\u003eQALY: Quality-Adjusted Life Year\u003c/p\u003e\n\u003cp\u003eRT: Radiotherapy\u003c/p\u003e\n\u003cp\u003eSE: Standard Error\u003c/p\u003e\n\u003cp\u003eTNBC: Triple-Negative Breast Cancer\u003c/p\u003e\n\u003cp\u003eUDC: Undiagnosed Cancer\u003c/p\u003e\n\u003cp\u003eVBA: Visual Basic for Applications\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate:\u0026nbsp;This study was based on aggregated, de-identified registry data from the Belgian Cancer Registry (BCR), which exempted it from ethical review.\u003cbr\u003e\u0026nbsp;Consent for publication:\u0026nbsp;Not applicable.\u003cbr\u003e\u0026nbsp;Availability of data and materials:\u0026nbsp;The Excel-based Markov model developed and analysed during the current study is provided as supplementary material with this article. In addition, a Word technical appendix (Additional file 1) is provided, containing detailed descriptions of the model structure, transition probabilities, utilities, healthcare costs, and assumptions used in the analysis.\u003c/p\u003e\n\u003cp\u003eCompeting interests:\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;This work was supported by Belgian Federal Science Policy Office, B2/202/P3/HELICON. The funder had no role in study design, analysis, interpretation, or manuscript writing.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions:\u0026nbsp;YK conceived and designed the study, developed the Markov model, collected data inputs, performed the analyses, and drafted the manuscript. NV supervised the economic modelling, and critically reviewed the manuscript. CM contributed to the study design, provided clinical expertise, and validated treatment pathways. KV provided input on epidemiological aspects and critically reviewed the manuscript. SG critically reviewed the manuscript. BD critically reviewed the manuscript. FV and HP ensured oncological accuracy in the model and critically reviewed the manuscript. DS supervised the project, provided methodological guidance, and critically reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements: The authors sincerely thank Dr. E.N. for her valuable clinical input on treatment protocols and cost estimations, which was instrumental in informing the healthcare cost calculations. Appreciation is also extended to all collaborators who contributed to the development of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHofmarcher T, Lindgren P, Wilking N, J\u0026ouml;nsson B. The cost of cancer in Europe 2018. Eur J Cancer. 1 avr 2020;129:41‑9. \u003c/li\u003e\n\u003cli\u003eJRC. 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Arr\u0026ecirc;t\u0026eacute; Royal du 13/05/2020 arrete royal n\u0026deg; 20 portant des mesures temporaires dans la lutte contre la pandemie covid-19 et visant a assurer la continuite des soins en matiere d\u0026rsquo;assurance obligatoire soins de sante [Internet]. Moniteur Belge; 2020 mai [cit\u0026eacute; 11 juill 2025]. Disponible sur: https://etaamb.openjustice.be/fr/arrete-royal-du-13-mai-2020_n2020041295\u003c/li\u003e\n\u003cli\u003eFaes C, Abrams S, Van Beckhoven D, Meyfroidt G, Vlieghe E, Hens N, et al. Time between Symptom Onset, Hospitalisation and Recovery or Death: Statistical Analysis of Belgian COVID-19 Patients. Int J Environ Res Public Health. 17 oct 2020;17(20):7560. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brcr","sideBox":"Learn more about [Breast Cancer Research](http://breast-cancer-research.biomedcentral.com)","snPcode":"13058","submissionUrl":"https://submission.nature.com/new-submission/13058/3","title":"Breast Cancer Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, COVID-19, Diagnostic delay, Molecular subtypes, Markov model, Health economics, QALY, Belgium","lastPublishedDoi":"10.21203/rs.3.rs-7786399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7786399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: During the first COVID-19 wave, breast cancer diagnoses declined sharply worldwide due to suspended screening programmes and delayed care-seeking driven by fear of infection. In Belgium, the national programme was halted from March to June 2020, leaving 135 invasive breast cancers undiagnosed. Although no stage shifts were observed in 2020, these undiagnosed cases risk later detection at more advanced stages, with worse prognosis, higher healthcare costs, and reduced health-related quality of life. Evidence indicates that such delays disproportionately affect aggressive subtypes (e.g., triple-negative (TNBC)) compared with slower-growing luminal-like cancers. This study projected the five-year impact of these diagnostic delays on health outcomes and costs, stratified by molecular subtype.\u003c/p\u003e\n\u003cp\u003eMethods: A Markov cohort model compared two cohorts of 10,147 Belgian women with breast cancer: a “disrupted-care” cohort (2020 data, including 135 undiagnosed cases) and a “non-disrupted” cohort (2017–2019 trends). Outcomes over five years were estimated from the healthcare payer perspective, including incremental QALYs, direct medical costs, and mortality. Data sources included the Belgian Cancer Registry, literature, and national cost databases. Sensitivity and scenario analyses assessed uncertainty.\u003cbr\u003e\nResults: Over five years, the diagnostic delays were projected to cause a total loss of 21 QALYs and €3.2M in additional healthcare costs across all subtypes, resulting in an estimated six additional deaths. This corresponds to a modest average impact of 0.002 QALYs and €315 per patient. The burden was disproportionately carried by aggressive subtypes. TNBC accounted for the largest health loss (-9.5 QALYs) and highest incremental costs (€1.6M), followed by HER2+ cancer (-2.5 QALYs; €0.5M). Probabilistic sensitivity analysis revealed considerable uncertainty in these estimates, particularly influenced by assumed input parameters.\u003c/p\u003e\n\u003cp\u003eConclusion: The impact of diagnostic delays during Belgium’s first COVID-19 wave was less severe than expected, likely because rapid recovery measures prevented a sustained stage shift. However, the overall modest results may mask a greater burden among faster-progressing subtypes such as TNBC and HER2+. The high uncertainty in the model underscores the need for better subtype-specific data. Ensuring diagnostic continuity, particularly for high-risk cancers, will be essential to mitigate the impact of future health system disruptions.\u003c/p\u003e","manuscriptTitle":"Subtype-specific health and economic impact of delayed breast cancer diagnosis during the early COVID-19 pandemic in Belgium: A Markov model analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 12:10:04","doi":"10.21203/rs.3.rs-7786399/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-21T16:32:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-18T08:00:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T16:18:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121242502725779612314537429948564817941","date":"2025-11-03T18:39:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121225246924559690251437925354497187290","date":"2025-11-02T00:05:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-17T14:51:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-07T10:44:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-07T08:53:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research","date":"2025-10-05T18:48:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brcr","sideBox":"Learn more about [Breast Cancer Research](http://breast-cancer-research.biomedcentral.com)","snPcode":"13058","submissionUrl":"https://submission.nature.com/new-submission/13058/3","title":"Breast Cancer Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38b82afd-47be-4279-943d-cc2133ce6c26","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T15:59:04+00:00","versionOfRecord":{"articleIdentity":"rs-7786399","link":"https://doi.org/10.1186/s13058-025-02207-2","journal":{"identity":"breast-cancer-research","isVorOnly":false,"title":"Breast Cancer Research"},"publishedOn":"2025-12-28 15:57:02","publishedOnDateReadable":"December 28th, 2025"},"versionCreatedAt":"2025-10-30 12:10:04","video":"","vorDoi":"10.1186/s13058-025-02207-2","vorDoiUrl":"https://doi.org/10.1186/s13058-025-02207-2","workflowStages":[]},"version":"v1","identity":"rs-7786399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7786399","identity":"rs-7786399","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}