Personalized neoadjuvant strategy using 70-gene assay in ER-positive/HER2- negative breast cancer to increase breast-conserving surgery rate (KBCSG016: PLATO trial)

Personalized neoadjuvant strategy using 70-gene assay in ER-positive/HER2- negative breast cancer to increase breast-conserving surgery rate (KBCSG016: PLATO trial) | 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 Article Personalized neoadjuvant strategy using 70-gene assay in ER-positive/HER2- negative breast cancer to increase breast-conserving surgery rate (KBCSG016: PLATO trial) Wonshik Han, Eunhye Kang, Ji Gwang Jung, Hong-Kyu Kim, Han-Byoel Lee, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6287262/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jun, 2025 Read the published version in npj Breast Cancer → Version 1 posted 11 You are reading this latest preprint version Abstract We investigated whether tailored neoadjuvant therapy (chemotherapy [NCT] or endocrine therapy [NET]) guided by a 70-gene assay could improve breast-conserving surgery (BCS) rates among patients with ER-positive/HER2-negative breast cancer initially deemed ineligible for BCS. Of 130 prospectively enrolled patients (stage II–IIIA, across four Korean centers), 92 were analyzed. Patients classified as high genomic risk received NCT, while low-risk patients underwent NET (letrozole ± leuprolide for premenopausal women) for 16–24 weeks. The primary endpoint—achieving the surgeon-defined target tumor size for BCS—was reached in 69.6% (95% CI: 59.1–78.7%), significantly surpassing the predefined goal of 50.8% (p < 0.05). Actual overall BCS rate was 59.8% (64.7% NCT, 45.8% NET). Pathologic complete response occurred in 2.2%, exclusively in the NCT group. Thus, pretreatment genomic profiling effectively guided therapy selection, substantially increasing BCS eligibility while sparing low-risk patients unnecessary chemotherapy toxicity. Biological sciences/Cancer/Breast cancer Biological sciences/Cancer/Cancer genomics Biological sciences/Cancer/Tumour biomarkers Health sciences/Biomarkers/Predictive markers Health sciences/Biomarkers/Prognostic markers Health sciences/Oncology/Surgical oncology 70-gene assay breast cancer breast conserving surgery neoadjuvant chemotherapy neoadjuvant endocrine therapy Figures Figure 1 Figure 2 Introduction For individuals diagnosed with operable breast cancer, neoadjuvant chemotherapy (NCT) is established as a conventional approach, particularly for those expected to undergo adjuvant chemotherapy. Notably, compared to adjuvant chemotherapy, a significant advantage of NCT is its ability to increase the likelihood of breast-conserving surgery (BCS), which is a key benefit of this treatment strategy. [ 1 – 3 ] Evidence from a meta-analysis of 14 studies indicates a 29% reduction in mastectomy rates among NCT recipients compared with that in patients receiving adjuvant chemotherapy without compromising local control. [ 4 ] This underscores the pivotal role of NCT in enhancing breast conservation opportunities. However, the effectiveness of NCT in treating oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer remains under scrutiny, owing to the lower response and lower incidence of pathologic complete response (pCR) within this subgroup. [ 5 ] Neoadjuvant endocrine therapy (NET) presents another option for ER+/HER2– breast cancer. In the P024 trial with NET, the BCS-conversion rate in postmenopausal women was 45% with letrozole and 35% with tamoxifen. [ 6 ] In the IMPACT trial, the BCS-conversion rate was 46% with neoadjuvant anastrozole and 22% with tamoxifen. [ 7 ] In the STAGE trial, neoadjuvant anastrozole + goserelin showed significantly better overall tumor response than tamoxifen + goserelin. [ 8 ] Additionally, multigene assays have shown promising efficacy in predicting NCT or NET response in ER+/HER2– breast cancer. For instance, the 21-gene assay (OncotypeDX, Exact Sciences, USA) had a high recurrence score (RS) and was significantly associated with pCR in NCT patients [ 9 ] and also inversely correlated with NET response. [ 10 , 11 ] Similarly, the 70-gene assay (MammaPrint; Agendia Inc., USA) used in this study has shown efficacy in identifying low-risk patients who may safely forgo chemotherapy and high-risk patients who can benefit from chemotherapy among patients with ER+/HER2– breast cancer. [ 12 ] This study aimed to explore the potential of tailored treatments (NCT or NET) guided by the 70-gene assay in increasing BCS rates in patients with ER+/HER2– breast cancer. Methods Study design and patients This PLATO study (NCT03900637) was a multicentre, phase II, prospective cohort study conducted from April 2019 to December 2023 across four large tertiary hospitals in South Korea (Seoul National University Hospital, Asan Medical Center, Seoul National University Bundang Hospital, and Korea Cancer Center Hospital). All participating centres received approval from their institutional review boards for this study. All patients provided written informed consent. The trial protocol is provided in Supplement 1. The main inclusion criteria were clinical stage II-IIIA, ER+/HER2– breast cancer with measurable tumour size, and BCS unfeasible considering the tumour size, tumour location, and breast size. The exclusion criteria were diffuse malignant microcalcification, multicentric breast cancer (multiple tumours in different quadrants), bilateral breast cancer, distant metastasis, history of breast treatment, history of other cancer, and male patients. Operating surgeons primarily decided BCS feasibility in each patient before inclusion in this study. Subsequently, imaging files (magnetic resonance imaging [MRI], mammography, and ultrasonography) and physical examination findings, if available, were independently reviewed by a panel of two independent experienced surgeons not involved in patient recruitment. The panel judged BCS feasibility and study eligibility. In cases of discordance between the two surgeons’ opinions, the images were evaluated by a third surgeon for the final decision. A total of 130 patients were initially screened, with 100 enrolled and 92 finally analysed (Fig. 1 ). Before therapy initiation, each surgeon recorded a target tumour size at which the surgeon could conduct BCS, considering the tumour location and breast size. This decision was predominantly based on MRI tumour size ( n = 86, 93·5%), and in a smaller proportion with ultrasonography size ( n = 6, 6·5%). Multigene assay and treatment allocation All patients underwent testing with the 70-gene assay (MammaPrint) using core needle biopsy specimens before neoadjuvant therapy initiation. Ten unstained slides of formalin-fixed paraffin-embedded tumour tissue were prepared for the 70-gene assay and sent to Agendia Inc. Patients were assigned to treatment based on the results of the 70-gene assay. Those classified as low-risk by the 70-gene assay received NET, while those classified as high-risk received NCT. The MammaPrint index was used to further categorise patients as UltraLow (UL; +1·000 to + 0·356), Low-Risk (LR; +0·355 to + 0·001), High 1 (H1; 0·000 to − 0·569), and High 2 (H2; − 0·570 to − 1·000). The NCT regimen included four standard cycles of anthracycline + cyclophosphamide (AC) every 3 weeks, followed by four cycles of docetaxel every 3 weeks or 12 cycles of paclitaxel weekly. In the NET regimen, postmenopausal women received letrozole (2·5 mg per day) for 16 weeks. Premenopausal women received leuprorelin (3·75 mg subcutaneously) every 4 weeks with letrozole for 16 weeks. The duration of NET could be extended to a maximum of 24 weeks based on physician’s decision. Evaluation of BCS conversion and actual BCS rate after neoadjuvant therapy completion BCS eligibility after NET or NCT was determined based on whether the residual tumour size on imaging after neoadjuvant therapies was equal to or smaller than the pre-established target size recorded by the surgeon. The choice between BCS and total mastectomy was made after discussions with patients, occasionally involving multidisciplinary team discussions. In cases where the resection margin was positive for tumour cells after BCS, the decision of re-excision or total mastectomy was made by the operating surgeon. Adjuvant therapy and follow-up After surgery, each patient received adjuvant therapy according to the guidelines of respective trial centres. Adjuvant endocrine therapy was recommended for all patients, whereas adjuvant chemotherapy was recommended for patients with disease progression during NET. For exploratory analysis, disease recurrence or survival would be monitored in post-surgery patients for a follow-up period of 5 years, according to institutional follow-up policy. Study endpoints The primary endpoint was achieving a rate of conversion, from BCS-ineligible to BCS-eligible, of more than 50·8%. The secondary endpoints included actual overall BCS rate, pCR rate, and clinical response rate. A previous study reported the conversion rate from BCS-ineligible to BCS-eligible with NCT of 35·8% in Korean patients with HR+/HER2– breast cancer. 13 Sample size calculations We assumed that with our study regimen, the BCS conversion rate would be increased to 50·8% (15% increase). Given these estimates, with 10% type II error rate and 90% power, the target enrolment was set at 122 patients. However, due to delays in patient enrolment, accrual was closed at 100 participants in December 2023. Statistical analysis Continuous variables are presented as median and interquartile range (IQR) and categorical variables as frequency and percentage. Continuous variables were compared between groups using the Wilcoxon rank sum test and categorical variables using Pearson’s chi-square test or Fisher’s exact test. A P -value < 0·05 was considered statistically significant. The rate of achieving target size and actual BCS rate were calculated with two-sided binominal confidence intervals (CIs) of 95% for the high-risk, low-risk, and overall groups. Differences between groups were compared using Pearson’s chi-square test or Fisher’s exact test. All statistical analyses were performed using R 4·3·0. Results Of the 100 patients enrolled, seven patients who were categorised as high-risk based on genomic assessment declined neoadjuvant chemotherapy and one patient who was lost to follow-up during therapy were excluded from the final analysis. The remaining 92 patients were finally included in the full analysis set. Among them, 68 patients (73·9%) were assigned to the genomic high-risk group (GH) and received NCT, whereas 24 (26·1%) patients were assigned to the genomic low-risk group (GL) and received NET (Fig. 1 ). Patient’s characteristics and initial demographics are shown in Table 1 . The median baseline tumour size on imaging was 3·7 cm (IQR 2·8–4·4 cm), with 87·0% of patients presenting as clinical T2 and 13·0% as clinical T3. The mean age of patients was 47·0 and 50·0 years and proportion of premenopausal patients was 64·7% and 62·5% in the GH and GL groups, respectively. Histological high-grade tumours were significantly more prevalent in the GH group than in the GL group (20·6% vs. 0%, P = 0·007). Table 1 Patient’s characteristics and initial tumor demographics Characteristics Total High-risk Low-risk P- value ( N = 92) ( n = 68) ( n = 24) Age, median (IQR), y 47.0 [43.5;56.5] 47.0 [43.5;56.0] 50.0 [43.5;57.5] 0.545 Menopause status 1 premenopausal 59 (64.1%) 44 (64.7%) 15 (62.5%) postmenopausal 33 (35.9%) 24 (35.3%) 9 (37.5%) BMI, median (IQR) 23.4 [21.2;26.2] 23.3 [21.1;25.5] 23.5 [21.3;26.9] 0.566 Tumor location 0.476 Right 46 (50.0%) 32 (47.1%) 14 (58.3%) Left 46 (50.0%) 36 (52.9%) 10 (41.7%) Tumor baseline size, median (IQR), cm 3.7 [2.8; 4.4] 3.6 [2.8; 4.2] 3.8 [3.3; 4.6] 0.499 Multiple tumor 1 Yes 6 (6.5%) 4 (5.9%) 2 (8.3%) No 86 (93.5%) 64 (94.1%) 22 (91.7%) cT stage 0.334 cT2 80 (87.0%) 61 (89.7%) 19 (79.2%) cT3 12 (13.0%) 7 (10.3%) 5 (20.8%) cN stage 0.519 cN0 47 (51.1%) 34 (50.0%) 13 (54.2%) cN1 39 (42.4%) 28 (41.2%) 11 (45.8%) cN2 6 (6.5%) 6 (8.9%) 0 (0.0%) Clinical Stage 0.839 cIIA 40 (43.5%) 30 (44.1%) 10 (41.7%) cIIB 42 (45.7%) 30 (44.1%) 12 (50.0%) cIIIA 10 (10.9%) 8 (11.8%) 2 (8.3%) Histologic Grade 0.007 Grade 1 2 (2.2%) 0 (0.0%) 2 (8.3%) Grade 2 67 (72.8%) 49 (72.1%) 18 (75.0%) Grade 3 14 (15.2%) 14 (20.6%) 0 (0.0%) Unknown 9 (9.8%) 5 (7.4%) 4 (16.7%) Progesterone Receptor 0.061 Positive 81 (88.0%) 57 (83.8%) 24 (100.0%) Negative 11 (12.0%) 11 (16.2%) 0 (0.0%) Ki67 0.61 ≥ 10 52 (56.5%) 40 (58.8%) 12 (50.0%) < 10 40 (43.5%) 28 (41.2%) 12 (50.0%) Abbreviations: IQR, interquartile range; BMI, body mass index. Table 2 summarises the neoadjuvant treatment response and surgery results in each treatment group. The end-of-treatment (EOT) tumour size on imaging was 2·2 cm (IQR 1·6–3·0 cm), with median tumour size of 2·1 cm in the GH group and 2·4 cm in the GL group ( P = 0·018). Clinically, 5·4% of patients exhibited complete response (CR), 73·9% showed partial response (PR), 19·6% had stable disease (SD), and 1·1% had progressive disease (PD). pCR was achieved in 2·2% of patients, all of whom were in the GH group. Table 2 Post-treatment tumor response and surgery method Characteristics Total High-risk Low-risk P -value ( N = 92) ( n = 68) ( n = 24) Baseline tumor size (imaging), median (IQR), cm 3.7 [2.8; 4.4] 3.6 [2.8; 4.2] 3.8 [3.3; 4.6] 0.499 End of treatment tumor size (imaging), median (IQR), cm 2.2 [1.6; 3.0] 2.1 [1.4; 2.8] 2.4 [2.0; 3.2] 0.018 Clinical response 0.139 CR 5 (5.4%) 5 (7.4%) 0 (0.0%) PR 68 (73.9%) 52 (76.5%) 16 (66.7%) SD 18 (19.6%) 10 (14.7%) 8 (33.3%) PD 1 (1.1%) 1 (1.5%) 0 (0.0%) Breast surgery 0.168 BCS 55 (59.8%) 44 (64.7%) 11 (45.8%) TM 37 (40.2%) 24 (35.3%) 13 (54.2%) Axilla surgery 1 SLNB 61 (66.3%) 45 (66.2%) 16 (66.7%) ALND 31 (33.7%) 23 (33.8%) 8 (33.3%) Pathologic tumor size, median (IQR), cm 2.2 [1.5; 3.3] 2.2 [1.1; 3.3] 2.1 [1.8; 3.2] 0.405 Pathologic T stage 0.731 T0 2 (2.2%) 2 (2.9%) 0 (0.0%) T1 34 (37.0%) 25 (36.8%) 9 (37.5%) T2 52 (56.5%) 38 (55.9%) 14 (58.3%) T3 2 (2.2%) 1 (1.5%) 1 (4.2%) T4 2 (2.2%) 2 (2.9%) 0 (0.0%) Pathologic N stage 0.068 N0 46 (50.0%) 39 (57.4%) 7 (29.2%) N1 35 (38.0%) 21 (30.9%) 14 (58.3%) N2 6 (6.5%) 5 (7.4%) 1 (4.2%) N3 5 (5.4%) 3 (4.4%) 2 (8.3%) Pathologic response 0.752 CR 2 (2.2%) 2 (2.9%) 0 (0.0%) PR 65 (70.7%) 47 (69.1%) 18 (75.0%) SD 19 (20.7%) 15 (22.1%) 4 (16.7%) PD 6 (6.5%) 4 (5.9%) 2 (8.3%) Reached target size 0.257 yes 64 (69.6%) 50 (73.5%) 14 (58.3%) no 28 (30.4%) 18 (26.5%) 10 (41.7%) End of treatment surgery plan 0.099 BCS 57 (62.0%) 46 (67.6%) 11 (45.8%) TM 35 (38.0%) 22 (32.4%) 13 (54.2%) Abbreviations: IQR, inter quartile range; CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease; BCS, breast conserving surgery; TM, total mastectomy; SLNB, sentinel lymph node biopsy; ALND, axillary lymph node dissection. The primary endpoint, i.e., achieving the pre-established target tumour size for BCS, was reached in 69·6% (64/92, 95% CI: 59·1%–78·7%) of patients, significantly surpassing the set goal of 50·8% ( P < 0·001). The rate was 73·5% in the GH and 58·3% in the GL group. The EOT surgical plan of BCS was 62·0% (57/92, 95% CI: 51·2%–71·9%; 67·6% for GH and 45·8% for GL). Finally, two of the 57 patients initially planned to undergo BCS eventually underwent total mastectomy due to positive resection margin. The actual overall BCS rate was 59·8% (55/92, 95% CI: 49·0%–69·9%; 64·7% for GH and 45·8% for GL) (Fig. 2 a). The overall response and choice of surgery were similar between premenopausal and postmenopausal patients (Supplement 2: eTable 1). Further, 142 adverse events were reported in 42 patients (35 patients with NCT [51·5%] and 7 patients with NET [29·2%]). Most of the reported adverse reactions were of grade 1 or 2, with no severe adverse events observed beyond expectations (Supplement2: eTable 2). None of the pre-treatment clinical or pathological factors significantly predicted BCS conversion (Supplement 2: eTable 3). Exploratory analysis according to four groups of MammaPrint index Exploratory analysis revealed that of the total 92 patients, 11 were categorised as H2 (12·0%), 57 as H1 (62·0%), 21 as LR (22·8%), and 3 as UL (3·2%). The H2 group showed a higher rate of achieving target size than the H1 group (90·9% vs. 70·2%), and the UL group showed higher rate than the LR group (100% vs. 52·4%), although neither difference was significant ( P > 0·05) (Fig. 2 b). Discussion This study evaluated the effectiveness of pre-treatment multigene assays in guiding NCT or NET to achieve improved BCS rates. The primary endpoint of achieving the pre-established target tumour size for BCS was reached in 69·6% patients with ER+/HER2– breast cancer initially deemed unsuitable for BCS. The PLATO study stands out from previous research in several key aspects. Notably, we engaged an independent panel of experienced surgeons to assess BCS feasibility and study eligibility, ensuring an unbiased evaluation process. Additionally, the requirement for pre-treatment determination of a target tumour size for BCS, primarily based on MRI, established a clear, objective benchmark for treatment efficacy. Furthermore, the flexibility allowed the extension of the period of NET beyond the standard 16 weeks to a maximum of 24 weeks, which introduced a tailored approach to patient care. Importantly, our study included a large proportion of premenopausal patients (64·1%), who generally exhibit a stronger preference for breast preservation. Similar to the PLATO study, Bear et al. performed a pilot study of 64 patients using the 21-gene assay (Oncotype DX) for guiding NCT or NET to facilitate BCS, [ 14 ] with primary endpoint not being BCS rate or BCS conversion rate but rather refusal rate of assigned treatment in the randomised patients. They reported a BCS-conversion rate of 72–75% with NET in patients with low or intermediate RS and 57–64% with NCT in patients with high or intermediate RS. The overall BCS-conversion rate was similar to that in our study, although our study showed a higher rate of BCS conversion in NCT than in NET. A critical limitation of this type of study on BCS conversion lies in the objective determination of BCS eligibility. In most studies, BCS eligibility was evaluated by operating surgeons. [ 14 , 15 ] To increase the objectivity of the judgment of BCS eligibility, we used the two unique processes described above: 1) a panel of three independent judges and 2) pre-recorded target tumour size for each patient. The tumour size is the most significant factor for the choice of total mastectomy versus BCS. A systematic review investigating factors influencing the choice of surgery found that rates of mastectomy increased with larger tumour size. [ 16 ] However, no absolute size threshold has been established. The type of surgery depends on factors such as tumour location in the breast, distance from the nipple, patient’s breast size, breast redundancy, patient’s age, and operating surgeon’s preference. [ 17 ] The utilisation of the 70-gene assay (MammaPrint) for genomic risk classification yielded a higher proportion of high-risk patients than anticipated, contrasting with expectations based on the MINDACT trial outcomes (64·1% genomic low-risk). [ 12 ] However, in our study, 73·9% of patients were classified as genomic high-risk, and the reason for this is uncertain. This might be due to the inclusion of clinically higher-risk patients in our study, with larger tumours and/or axillary lymph node involvement, because only patients needing neoadjuvant therapy and total mastectomy candidates could be included. Another potential factor contributing to the higher-risk classification could be the use of core biopsy specimens available before neoadjuvant therapy rather than the use of surgical specimens. In a study that used 70-gene assay for core biopsy specimens of patients receiving neoadjuvant chemotherapy, 86% were classified as genomic high-risk. [ 18 ] In a study analysing National Cancer Database of USA of patients who received neoadjuvant chemotherapy, 84·6% of patients were high-risk with 70-gene assay, while 57·7% were high-risk with 21-gene assay (Oncotype DX). [ 19 ] In the study by Bear et al., which also used core biopsy specimens for the 21-gene assay to choose neoadjuvant therapy, only 23·7% patients were high-risk. [ 14 ] Furthermore, Audeh et al. conducted 70-gene assay of patients in the Neoadjuvant Breast Symphony Trial (NBRST) and showed that 76·8% patients were classified as high-risk. [ 20 ] The high proportion of high-risk results can be a disadvantage for using the 70-gene assay for individualised strategy selection of NCT vs. NET, given that more patients have to receive chemotherapy. This study included a relatively high proportion of premenopausal women (64·1%) and showed that this strategy could be helpful for young women with a strong desire for breast conservation. The overall response and rate of achieving target size were similar between premenopausal and postmenopausal women. However, concerns persist regarding the use of multigene assays in premenopausal women. In an exploratory analysis of updated results of MINDACT trial, women aged ≤ 50 years with high-clinical/low-genomic risk (70-gene assay) had an absolute distant metastasis-free survival benefit of 5% at 8 years with the addition of adjuvant chemotherapy. [ 21 ] Furthermore, in the RxPONDER study for lymph node-positive patients, premenopausal women had significant chemotherapy benefits even with low 21-gene RS. [ 22 ] The American Society of Clinical Oncology guideline update for biomarkers published in 2022 recommended that clinicians should not use the MammaPrint test for patients aged ≤ 50 years, and Oncotype DX test should not be offered to premenopausal node-positive patients. [ 23 ] Future clinical trials will shed light on whether ovarian function suppression would replace chemotherapy in these patients. In our study, we could observe endocrine therapy response in low-risk patients and recommend adjuvant chemotherapy in cases of disease progression during NET as a second safety check. There are several disadvantages associated with implementing this strategy in clinical practice. We used eight cycles of preoperative anthracyclines and taxanes in our study, and 50% of the patients were found to be lymph node-negative at surgery. A significant proportion of these patients might have been true lymph node-negative before neoadjuvant therapies, as lymph node complete remission is uncommon in ER+/HER2– population. [ 24 ] If we had treated these patients with surgery first rather than neoadjuvant therapy, the lymph node-negative patients would have received four cycles of adjuvant chemotherapy rather than eight cycles. Moreover, 11·9% of patients in our study had pN2 or N3 disease, which is not an indication of using genomic assays according to current guidelines. [ 23 , 25 ] We did not find any clinical or presurgical pathological factors significantly associated with BCS conversion. Although not statistically significant due to the small sample size, there were numerical differences according to the four-level classification (subcategories) of MammaPrint index. Among genomic high-risk patients receiving NCT, target size achievement rate and actual BCS rate were higher in H2 patients than in H1 patients, and among genomic low-risk patients receiving NET, UL showed higher BCS conversion than LR. Consistent with our study findings, a previous study showed a significantly higher percentage of pCR in H2 tumours (23%) than in H1 tumours (6·1%) in NCT-treated patients in the NBRST. [ 26 ] To the best of our knowledge, our study is the first to show the possibility of better response to NET in UL patients than in LR patients. The target size achievement rate was 100·0% in UL compared with the 52·4% in LR. This study has some limitations. First, we were unable to recruit the preplanned number of patients due to delay in patient enrolment. Second, our study was not randomised and lacked a control arm; we used historical data as control. This study included only Asian women with relatively small-sized breasts. The general breast conservation rate for early breast cancer in Korea was 68·6% in 2019. [ 27 ] The preference for breast conservation is different across countries. In the CALGB 40603 study, only 68% of women who converted from BCS-ineligible to -eligible with neoadjuvant therapy chose breast conservation. [ 15 ] In contrast, 86·9% of BCS-converted patients with NCT chose BCS in a Korean study. [ 13 ] In the BrighTNess study, 79·6% of BCS-eligible European and Asian patients chose BCS after neoadjuvant therapy in contrast to 55·0% of North American patients. [ 28 ] Our strategy might not be highly applicable in North America and other countries where BCS rate is low. In conclusion, for women with ER+/HER2– breast cancer seeking breast preservation but facing challenges with borderline or impossible BCS mainly due to tumour size, our study recommends pre-treatment multigene assays to guide the choice between NCT and NET. This approach significantly increases the chances of achieving BCS while avoiding unnecessary chemotherapy in patients where it is not needed. Our study highlights the feasibility of this strategy in clinical practice. Declarations Author Contribution Dr. Han W. had full access to all of the data in the study and take responsibility for the integrity and the accuracy of the data analysis.Concept and design: Han W, Jung JGAcquisition, analysis, or interpretation of data: Han W, Kang E, Jung JG, Kim HK, Lee HB, Kim J, Shin HC, Kim HA, Kim EK, and Son BHDrafting of the manuscript: Han W, Kang ECritical review of the manuscript for important intellectual content: Han W, Son BH, Kim EK, Kim HAStatistical analysis: Kang E, The Medical Research Collaborating Center (MRCC) of Seoul National University Hospital Biomedical Research InstituteObtained funding: Han WAdministrative, technical, or material support: Kang E, Jung JG, Kim HK, Lee HB, Kim J, LEE SB, Park CS, Seong MKSupervision: Son BH, Kim EK, Kim HA Funding: This research received financial support and drugs from the following companies: Takeda Pharmaceutical Co., Ltd., Kwang Dong Pharmaceutical Co., Ltd., Shin Poong Pharmaceutical Co., Ltd., HyupJin Corporation, and Agendia, Inc. Presentation: This study was presented as a poster in 2024 ASCO Annual Meeting (Abstract Number: 595). Declaration of conflict of interests Han W and Lee HB are co-founders and members of the DCGen Co., Ltd board of directors. Lee HB received research funding from Devicor Medical Product, Inc. and consulting fees from Need Inc., outside the current work. Data deposition and materials sharing Data available: Yes Data types: Deidentified participant data How to access data: A Research Collaboration Proposal Request Form can be submitted to Dr. Wonshik Han ( [email protected] ) or Byung Ho Son ( [email protected] ) to be considered for collaboration. When available: With publication Additional Information Who can access the data: Researchers who provide a methodologically sound proposal to achieve aims in the approved proposal Types of analyses: Specified purposes only Mechanisms of data availability: With signed data access agreement. Any additional restrictions: N/A. 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Actual conversion rate from total mastectomy to breast conservation after neoadjuvant chemotherapy for stages II–III breast cancer patients. J Breast Dis 2017; 5 : 51–6. Bear HD, Wan W, Robidoux A, Rubin P, Limentani S, White RL, et al. Using the 21-gene assay from core needle biopsies to choose neoadjuvant therapy for breast cancer: A multicenter trial. J Surg Oncol 2017; 115 : 917–23. Golshan M, Cirrincione CT, Sikov WM, Berry DA, Jasinski S, Weisberg TF, et al. Impact of neoadjuvant chemotherapy in stage II–III triple negative breast cancer on eligibility for breast-conserving surgery and breast conservation rates: surgical results from CALGB 40603 (Alliance). Ann Surg 2015; 262 : 434–9; discussion 438–9. Gu J, Groot G, Boden C, Busch A, Holtslander L, Lim H. Review of factors influencing women’s choice of mastectomy versus breast conserving therapy in early stage breast cancer: a systematic review. Clin Breast Cancer 2018; 18 : e539–54. Han Y, Jung JG, Kim J-I, Lim C, Kim H-K, Lee H-B, et al. The percentage of unnecessary mastectomy due to false size prediction using preoperative ultrasonography and MRI in breast cancer patients who underwent neoadjuvant chemotherapy: a prospective cohort study. Int J Surg 2023; 109 : 3993–9. Straver ME, Glas AM, Hannemann J, Wesseling J, van de Vijver MJ, Rutgers EJT, et al. The 70-gene signature as a response predictor for neoadjuvant chemotherapy in breast cancer. Breast Cancer Res Treat 2010; 119 : 551–8. Freeman JQ, Shubeck S, Howard FM, Chen N, Nanda R, Huo D. Evaluation of multigene assays as predictors for response to neoadjuvant chemotherapy in early-stage breast cancer patients. npj Breast Cancer 2023; 9 : 33. Audeh W, Ramaswamy H, Menicucci A, FLEX Investigators’ Group (2024) Prediction of chemotherapy benefit by MammaPrint® in patients with HR+HER2- early-stage breast cancer from real-world evidence studies. Miami; 2024. van Piccart M, van ’t Veer LJ, Poncet C, Lopes Cardozo JMN, Delaloge S, Pierga JY et al. 70-gene signature as an aid for treatment decisions in early breast cancer: updated results of the phase 3 randomised MINDACT trial with an exploratory analysis by age. Lancet Oncol 2021; 22 : 476–88. Kalinsky K, Barlow WE, Gralow JR, Meric-Bernstam F, Albain KS, Hayes DF, et al. 21-gene assay to inform chemotherapy benefit in node-positive breast cancer. N Engl J Med 2021; 385 : 2336–47. Andre F, Ismaila N, Allison KH, Barlow WE, Collyar DE, Damodaran S, et al. Biomarkers for adjuvant endocrine and chemotherapy in early-stage breast cancer: ASCO guideline update. J Clin Oncol 2022; 40 : 1816–37. Kim HJ, Noh WC, Lee ES, Jung YS, Kim LS, Han W, et al. Efficacy of neoadjuvant endocrine therapy compared with neoadjuvant chemotherapy in pre-menopausal patients with oestrogen receptor-positive and HER2-negative, lymph node-positive breast cancer. Breast Cancer Res 2020; 22 : 54. NCCN clinical practice guideline in oncology: breast cancer. ver. 2.2024. National Comprehensive Cancer Network. Beitsch PD, Pellicane JV, Pusztai L, Baron P, Cobain EF, Murray MK, et al. MammaPrint Index as a predictive biomarker for neoadjuvant chemotherapy response and outcome in patients with HR+HER2- breast cancer in NBRST. J Clin Oncol 2023; 41 (16_suppl): 521. Choi JE, Kim Z, Park CS, Park EH, Lee SB, Lee SK, et al. Breast cancer statistics in Korea, 2019. J Breast Cancer 2023; 26 : 207–20. Golshan M, Loibl S, Wong SM, Houber JB, O’Shaughnessy J, Rugo HS, et al. Breast conservation after neoadjuvant chemotherapy for triple-negative breast cancer: surgical results from the BrighTNess randomized clinical trial. JAMA Surg 2020; 155 : e195410. Additional Declarations Competing interest reported. Han W and Lee HB are co-founders and members of the DCGen Co., Ltd board of directors. Lee HB received research funding from Devicor Medical Product, Inc. and consulting fees from Need Inc., outside the current work. Supplementary Files Supplement1PLATOprotocol.docx Supplement2.eTable.docx Cite Share Download PDF Status: Published Journal Publication published 20 Jun, 2025 Read the published version in npj Breast Cancer → Version 1 posted Editorial decision: Revision requested 13 May, 2025 Reviewers agreed at journal 07 May, 2025 Reviews received at journal 05 May, 2025 Reviewers agreed at journal 05 May, 2025 Reviews received at journal 21 Apr, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers invited by journal 02 Apr, 2025 Editor assigned by journal 31 Mar, 2025 Submission checks completed at journal 30 Mar, 2025 First submitted to journal 23 Mar, 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. 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University Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Eunhye","middleName":"","lastName":"Kang","suffix":""},{"id":445764500,"identity":"675def6f-e6ef-4729-b2f5-685ca5ac786d","order_by":2,"name":"Ji Gwang Jung","email":"","orcid":"","institution":"Seoul National University Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ji","middleName":"Gwang","lastName":"Jung","suffix":""},{"id":445764501,"identity":"da740090-6161-4f72-a574-e55c67425215","order_by":3,"name":"Hong-Kyu Kim","email":"","orcid":"","institution":"Seoul National University Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hong-Kyu","middleName":"","lastName":"Kim","suffix":""},{"id":445764502,"identity":"8740e599-8456-45b2-b169-e1b6c4d211a1","order_by":4,"name":"Han-Byoel Lee","email":"","orcid":"","institution":"Seoul National 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University","correspondingAuthor":true,"prefix":"","firstName":"Byung","middleName":"Ho","lastName":"Son","suffix":""}],"badges":[],"createdAt":"2025-03-23 08:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6287262/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6287262/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41523-025-00772-5","type":"published","date":"2025-06-20T15:57:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81936025,"identity":"3bd67aa5-aba2-43b6-8102-b4ae8ec501e0","added_by":"auto","created_at":"2025-05-05 06:02:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1147034,"visible":true,"origin":"","legend":"\u003cp\u003eCONSORT flow diagram\u003c/p\u003e","description":"","filename":"figure1new.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6287262/v1/4591ea7fc8455726725151c3.jpg"},{"id":81932793,"identity":"42b097cc-be76-4747-aafd-c3003935b771","added_by":"auto","created_at":"2025-05-05 05:37:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":437794,"visible":true,"origin":"","legend":"\u003cp\u003eTarget size achievement rate and actual BCS rate stratified by genomic risk\u003c/p\u003e","description":"","filename":"figure2new.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6287262/v1/365cd74afd311ffdf61b0256.jpg"},{"id":85231407,"identity":"57b154b1-b56f-4269-bfbe-2e190beec8ee","added_by":"auto","created_at":"2025-06-23 16:07:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2552528,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6287262/v1/0834ddea-c4c7-4656-9110-792f7d2502f2.pdf"},{"id":81936228,"identity":"9821fe30-f8aa-41ee-b8f7-7e466c4625f3","added_by":"auto","created_at":"2025-05-05 06:06:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":129349,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1PLATOprotocol.docx","url":"https://assets-eu.researchsquare.com/files/rs-6287262/v1/ee5651007e6b61cd2722d1a2.docx"},{"id":81932785,"identity":"d0977ad3-404c-45c7-a773-032f22546753","added_by":"auto","created_at":"2025-05-05 05:37:29","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15086,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement2.eTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-6287262/v1/6b8b9370596b75a88b550018.docx"}],"financialInterests":"Competing interest reported. Han W and Lee HB are co-founders and members of the DCGen Co., Ltd board of directors. Lee HB received research funding from Devicor Medical Product, Inc. and consulting fees from Need Inc., outside the current work.","formattedTitle":"Personalized neoadjuvant strategy using 70-gene assay in ER-positive/HER2- negative breast cancer to increase breast-conserving surgery rate (KBCSG016: PLATO trial)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFor individuals diagnosed with operable breast cancer, neoadjuvant chemotherapy (NCT) is established as a conventional approach, particularly for those expected to undergo adjuvant chemotherapy. Notably, compared to adjuvant chemotherapy, a significant advantage of NCT is its ability to increase the likelihood of breast-conserving surgery (BCS), which is a key benefit of this treatment strategy.\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR29\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e Evidence from a meta-analysis of 14 studies indicates a 29% reduction in mastectomy rates among NCT recipients compared with that in patients receiving adjuvant chemotherapy without compromising local control.\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e This underscores the pivotal role of NCT in enhancing breast conservation opportunities. However, the effectiveness of NCT in treating oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer remains under scrutiny, owing to the lower response and lower incidence of pathologic complete response (pCR) within this subgroup.\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eNeoadjuvant endocrine therapy (NET) presents another option for ER+/HER2\u0026ndash; breast cancer. In the P024 trial with NET, the BCS-conversion rate in postmenopausal women was 45% with letrozole and 35% with tamoxifen.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e In the IMPACT trial, the BCS-conversion rate was 46% with neoadjuvant anastrozole and 22% with tamoxifen.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e In the STAGE trial, neoadjuvant anastrozole\u0026thinsp;+\u0026thinsp;goserelin showed significantly better overall tumor response than tamoxifen\u0026thinsp;+\u0026thinsp;goserelin.\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e Additionally, multigene assays have shown promising efficacy in predicting NCT or NET response in ER+/HER2\u0026ndash; breast cancer. For instance, the 21-gene assay (OncotypeDX, Exact Sciences, USA) had a high recurrence score (RS) and was significantly associated with pCR in NCT patients \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e and also inversely correlated with NET response.\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e Similarly, the 70-gene assay (MammaPrint; Agendia Inc., USA) used in this study has shown efficacy in identifying low-risk patients who may safely forgo chemotherapy and high-risk patients who can benefit from chemotherapy among patients with ER+/HER2\u0026ndash; breast cancer.\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study aimed to explore the potential of tailored treatments (NCT or NET) guided by the 70-gene assay in increasing BCS rates in patients with ER+/HER2\u0026ndash; breast cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patients\u003c/h2\u003e \u003cp\u003eThis PLATO study (NCT03900637) was a multicentre, phase II, prospective cohort study conducted from April 2019 to December 2023 across four large tertiary hospitals in South Korea (Seoul National University Hospital, Asan Medical Center, Seoul National University Bundang Hospital, and Korea Cancer Center Hospital). All participating centres received approval from their institutional review boards for this study. All patients provided written informed consent. The trial protocol is provided in Supplement 1.\u003c/p\u003e \u003cp\u003eThe main inclusion criteria were clinical stage II-IIIA, ER+/HER2\u0026ndash; breast cancer with measurable tumour size, and BCS unfeasible considering the tumour size, tumour location, and breast size. The exclusion criteria were diffuse malignant microcalcification, multicentric breast cancer (multiple tumours in different quadrants), bilateral breast cancer, distant metastasis, history of breast treatment, history of other cancer, and male patients. Operating surgeons primarily decided BCS feasibility in each patient before inclusion in this study. Subsequently, imaging files (magnetic resonance imaging [MRI], mammography, and ultrasonography) and physical examination findings, if available, were independently reviewed by a panel of two independent experienced surgeons not involved in patient recruitment. The panel judged BCS feasibility and study eligibility. In cases of discordance between the two surgeons\u0026rsquo; opinions, the images were evaluated by a third surgeon for the final decision. A total of 130 patients were initially screened, with 100 enrolled and 92 finally analysed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Before therapy initiation, each surgeon recorded a target tumour size at which the surgeon could conduct BCS, considering the tumour location and breast size. This decision was predominantly based on MRI tumour size (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;86, 93\u0026middot;5%), and in a smaller proportion with ultrasonography size (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, 6\u0026middot;5%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultigene assay and treatment allocation\u003c/h3\u003e\n\u003cp\u003eAll patients underwent testing with the 70-gene assay (MammaPrint) using core needle biopsy specimens before neoadjuvant therapy initiation. Ten unstained slides of formalin-fixed paraffin-embedded tumour tissue were prepared for the 70-gene assay and sent to Agendia Inc. Patients were assigned to treatment based on the results of the 70-gene assay. Those classified as low-risk by the 70-gene assay received NET, while those classified as high-risk received NCT. The MammaPrint index was used to further categorise patients as UltraLow (UL; +1\u0026middot;000 to +\u0026thinsp;0\u0026middot;356), Low-Risk (LR; +0\u0026middot;355 to +\u0026thinsp;0\u0026middot;001), High 1 (H1; 0\u0026middot;000 to \u0026minus;\u0026thinsp;0\u0026middot;569), and High 2 (H2; \u0026minus;\u0026thinsp;0\u0026middot;570 to \u0026minus;\u0026thinsp;1\u0026middot;000).\u003c/p\u003e \u003cp\u003eThe NCT regimen included four standard cycles of anthracycline\u0026thinsp;+\u0026thinsp;cyclophosphamide (AC) every 3 weeks, followed by four cycles of docetaxel every 3 weeks or 12 cycles of paclitaxel weekly. In the NET regimen, postmenopausal women received letrozole (2\u0026middot;5 mg per day) for 16 weeks. Premenopausal women received leuprorelin (3\u0026middot;75 mg subcutaneously) every 4 weeks with letrozole for 16 weeks. The duration of NET could be extended to a maximum of 24 weeks based on physician\u0026rsquo;s decision.\u003c/p\u003e\n\u003ch3\u003eEvaluation of BCS conversion and actual BCS rate after neoadjuvant therapy completion\u003c/h3\u003e\n\u003cp\u003eBCS eligibility after NET or NCT was determined based on whether the residual tumour size on imaging after neoadjuvant therapies was equal to or smaller than the pre-established target size recorded by the surgeon. The choice between BCS and total mastectomy was made after discussions with patients, occasionally involving multidisciplinary team discussions. In cases where the resection margin was positive for tumour cells after BCS, the decision of re-excision or total mastectomy was made by the operating surgeon.\u003c/p\u003e\n\u003ch3\u003eAdjuvant therapy and follow-up\u003c/h3\u003e\n\u003cp\u003e After surgery, each patient received adjuvant therapy according to the guidelines of respective trial centres. Adjuvant endocrine therapy was recommended for all patients, whereas adjuvant chemotherapy was recommended for patients with disease progression during NET. For exploratory analysis, disease recurrence or survival would be monitored in post-surgery patients for a follow-up period of 5 years, according to institutional follow-up policy.\u003c/p\u003e\n\u003ch3\u003eStudy endpoints\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint was achieving a rate of conversion, from BCS-ineligible to BCS-eligible, of more than 50\u0026middot;8%. The secondary endpoints included actual overall BCS rate, pCR rate, and clinical response rate. A previous study reported the conversion rate from BCS-ineligible to BCS-eligible with NCT of 35\u0026middot;8% in Korean patients with HR+/HER2\u0026ndash; breast cancer.\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSample size calculations\u003c/h2\u003e \u003cp\u003eWe assumed that with our study regimen, the BCS conversion rate would be increased to 50\u0026middot;8% (15% increase). Given these estimates, with 10% type II error rate and 90% power, the target enrolment was set at 122 patients. However, due to delays in patient enrolment, accrual was closed at 100 participants in December 2023.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as median and interquartile range (IQR) and categorical variables as frequency and percentage. Continuous variables were compared between groups using the Wilcoxon rank sum test and categorical variables using Pearson\u0026rsquo;s chi-square test or Fisher\u0026rsquo;s exact test. A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eThe rate of achieving target size and actual BCS rate were calculated with two-sided binominal confidence intervals (CIs) of 95% for the high-risk, low-risk, and overall groups. Differences between groups were compared using Pearson\u0026rsquo;s chi-square test or Fisher\u0026rsquo;s exact test. All statistical analyses were performed using R 4\u0026middot;3\u0026middot;0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf the 100 patients enrolled, seven patients who were categorised as high-risk based on genomic assessment declined neoadjuvant chemotherapy and one patient who was lost to follow-up during therapy were excluded from the final analysis. The remaining 92 patients were finally included in the full analysis set. Among them, 68 patients (73\u0026middot;9%) were assigned to the genomic high-risk group (GH) and received NCT, whereas 24 (26\u0026middot;1%) patients were assigned to the genomic low-risk group (GL) and received NET (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatient\u0026rsquo;s characteristics and initial demographics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median baseline tumour size on imaging was 3\u0026middot;7 cm (IQR 2\u0026middot;8\u0026ndash;4\u0026middot;4 cm), with 87\u0026middot;0% of patients presenting as clinical T2 and 13\u0026middot;0% as clinical T3. The mean age of patients was 47\u0026middot;0 and 50\u0026middot;0 years and proportion of premenopausal patients was 64\u0026middot;7% and 62\u0026middot;5% in the GH and GL groups, respectively. Histological high-grade tumours were significantly more prevalent in the GH group than in the GL group (20\u0026middot;6% vs. 0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u0026middot;007).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient\u0026rsquo;s characteristics and initial tumor demographics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (IQR), y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.0 [43.5;56.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.0 [43.5;56.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.0 [43.5;57.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopause status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epremenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59 (64.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.4 [21.2;26.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.3 [21.1;25.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.5 [21.3;26.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (52.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor baseline size, median (IQR), cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.7 [2.8; 4.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.6 [2.8; 4.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.8 [3.3; 4.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86 (93.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64 (94.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (91.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80 (87.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61 (89.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (79.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47 (51.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (54.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (44.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecIIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (44.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecIIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67 (72.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (72.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgesterone Receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81 (88.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57 (83.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52 (56.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: IQR, interquartile range; BMI, body mass index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarises the neoadjuvant treatment response and surgery results in each treatment group. The end-of-treatment (EOT) tumour size on imaging was 2\u0026middot;2 cm (IQR 1\u0026middot;6\u0026ndash;3\u0026middot;0 cm), with median tumour size of 2\u0026middot;1 cm in the GH group and 2\u0026middot;4 cm in the GL group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0\u0026middot;018). Clinically, 5\u0026middot;4% of patients exhibited complete response (CR), 73\u0026middot;9% showed partial response (PR), 19\u0026middot;6% had stable disease (SD), and 1\u0026middot;1% had progressive disease (PD). pCR was achieved in 2\u0026middot;2% of patients, all of whom were in the GH group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePost-treatment tumor response and surgery method\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline tumor size (imaging), median (IQR), cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.7 [2.8; 4.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.6 [2.8; 4.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.8 [3.3; 4.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnd of treatment \u003c/p\u003e \u003cp\u003etumor size (imaging), median (IQR), cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2 [1.6; 3.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.1 [1.4; 2.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.4 [2.0; 3.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68 (73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (76.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55 (59.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (40.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (54.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxilla surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLNB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61 (66.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (66.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (33.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic tumor size, median (IQR), cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2 [1.5; 3.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2 [1.1; 3.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1 [1.8; 3.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52 (56.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38 (55.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (57.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65 (70.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (69.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReached target size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64 (69.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnd of treatment \u003c/p\u003e \u003cp\u003esurgery plan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57 (62.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (67.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (54.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: IQR, inter quartile range; CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease; BCS, breast conserving surgery; TM, total mastectomy; SLNB, sentinel lymph node biopsy; ALND, axillary lymph node dissection.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe primary endpoint, i.e., achieving the pre-established target tumour size for BCS, was reached in 69\u0026middot;6% (64/92, 95% CI: 59\u0026middot;1%\u0026ndash;78\u0026middot;7%) of patients, significantly surpassing the set goal of 50\u0026middot;8% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001). The rate was 73\u0026middot;5% in the GH and 58\u0026middot;3% in the GL group. The EOT surgical plan of BCS was 62\u0026middot;0% (57/92, 95% CI: 51\u0026middot;2%\u0026ndash;71\u0026middot;9%; 67\u0026middot;6% for GH and 45\u0026middot;8% for GL). Finally, two of the 57 patients initially planned to undergo BCS eventually underwent total mastectomy due to positive resection margin. The actual overall BCS rate was 59\u0026middot;8% (55/92, 95% CI: 49\u0026middot;0%\u0026ndash;69\u0026middot;9%; 64\u0026middot;7% for GH and 45\u0026middot;8% for GL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The overall response and choice of surgery were similar between premenopausal and postmenopausal patients (Supplement 2: eTable 1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther, 142 adverse events were reported in 42 patients (35 patients with NCT [51\u0026middot;5%] and 7 patients with NET [29\u0026middot;2%]). Most of the reported adverse reactions were of grade 1 or 2, with no severe adverse events observed beyond expectations (Supplement2: eTable 2). None of the pre-treatment clinical or pathological factors significantly predicted BCS conversion (Supplement 2: eTable 3).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExploratory analysis according to four groups of MammaPrint index\u003c/h2\u003e \u003cp\u003eExploratory analysis revealed that of the total 92 patients, 11 were categorised as H2 (12\u0026middot;0%), 57 as H1 (62\u0026middot;0%), 21 as LR (22\u0026middot;8%), and 3 as UL (3\u0026middot;2%). The H2 group showed a higher rate of achieving target size than the H1 group (90\u0026middot;9% vs. 70\u0026middot;2%), and the UL group showed higher rate than the LR group (100% vs. 52\u0026middot;4%), although neither difference was significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0\u0026middot;05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the effectiveness of pre-treatment multigene assays in guiding NCT or NET to achieve improved BCS rates. The primary endpoint of achieving the pre-established target tumour size for BCS was reached in 69\u0026middot;6% patients with ER+/HER2\u0026ndash; breast cancer initially deemed unsuitable for BCS.\u003c/p\u003e \u003cp\u003eThe PLATO study stands out from previous research in several key aspects. Notably, we engaged an independent panel of experienced surgeons to assess BCS feasibility and study eligibility, ensuring an unbiased evaluation process. Additionally, the requirement for pre-treatment determination of a target tumour size for BCS, primarily based on MRI, established a clear, objective benchmark for treatment efficacy. Furthermore, the flexibility allowed the extension of the period of NET beyond the standard 16 weeks to a maximum of 24 weeks, which introduced a tailored approach to patient care. Importantly, our study included a large proportion of premenopausal patients (64\u0026middot;1%), who generally exhibit a stronger preference for breast preservation.\u003c/p\u003e \u003cp\u003eSimilar to the PLATO study, Bear et al. performed a pilot study of 64 patients using the 21-gene assay (Oncotype DX) for guiding NCT or NET to facilitate BCS,\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e with primary endpoint not being BCS rate or BCS conversion rate but rather refusal rate of assigned treatment in the randomised patients. They reported a BCS-conversion rate of 72\u0026ndash;75% with NET in patients with low or intermediate RS and 57\u0026ndash;64% with NCT in patients with high or intermediate RS. The overall BCS-conversion rate was similar to that in our study, although our study showed a higher rate of BCS conversion in NCT than in NET.\u003c/p\u003e \u003cp\u003eA critical limitation of this type of study on BCS conversion lies in the objective determination of BCS eligibility. In most studies, BCS eligibility was evaluated by operating surgeons.\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e To increase the objectivity of the judgment of BCS eligibility, we used the two unique processes described above: 1) a panel of three independent judges and 2) pre-recorded target tumour size for each patient. The tumour size is the most significant factor for the choice of total mastectomy versus BCS. A systematic review investigating factors influencing the choice of surgery found that rates of mastectomy increased with larger tumour size.\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e However, no absolute size threshold has been established. The type of surgery depends on factors such as tumour location in the breast, distance from the nipple, patient\u0026rsquo;s breast size, breast redundancy, patient\u0026rsquo;s age, and operating surgeon\u0026rsquo;s preference.\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe utilisation of the 70-gene assay (MammaPrint) for genomic risk classification yielded a higher proportion of high-risk patients than anticipated, contrasting with expectations based on the MINDACT trial outcomes (64\u0026middot;1% genomic low-risk).\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e However, in our study, 73\u0026middot;9% of patients were classified as genomic high-risk, and the reason for this is uncertain. This might be due to the inclusion of clinically higher-risk patients in our study, with larger tumours and/or axillary lymph node involvement, because only patients needing neoadjuvant therapy and total mastectomy candidates could be included. Another potential factor contributing to the higher-risk classification could be the use of core biopsy specimens available before neoadjuvant therapy rather than the use of surgical specimens. In a study that used 70-gene assay for core biopsy specimens of patients receiving neoadjuvant chemotherapy, 86% were classified as genomic high-risk.\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e In a study analysing National Cancer Database of USA of patients who received neoadjuvant chemotherapy, 84\u0026middot;6% of patients were high-risk with 70-gene assay, while 57\u0026middot;7% were high-risk with 21-gene assay (Oncotype DX).\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e In the study by Bear et al., which also used core biopsy specimens for the 21-gene assay to choose neoadjuvant therapy, only 23\u0026middot;7% patients were high-risk.\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e Furthermore, Audeh et al. conducted 70-gene assay of patients in the Neoadjuvant Breast Symphony Trial (NBRST) and showed that 76\u0026middot;8% patients were classified as high-risk.\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e The high proportion of high-risk results can be a disadvantage for using the 70-gene assay for individualised strategy selection of NCT vs. NET, given that more patients have to receive chemotherapy.\u003c/p\u003e \u003cp\u003eThis study included a relatively high proportion of premenopausal women (64\u0026middot;1%) and showed that this strategy could be helpful for young women with a strong desire for breast conservation. The overall response and rate of achieving target size were similar between premenopausal and postmenopausal women. However, concerns persist regarding the use of multigene assays in premenopausal women. In an exploratory analysis of updated results of MINDACT trial, women aged\u0026thinsp;\u0026le;\u0026thinsp;50 years with high-clinical/low-genomic risk (70-gene assay) had an absolute distant metastasis-free survival benefit of 5% at 8 years with the addition of adjuvant chemotherapy.\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e Furthermore, in the RxPONDER study for lymph node-positive patients, premenopausal women had significant chemotherapy benefits even with low 21-gene RS.\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e The American Society of Clinical Oncology guideline update for biomarkers published in 2022 recommended that clinicians should not use the MammaPrint test for patients aged\u0026thinsp;\u0026le;\u0026thinsp;50 years, and Oncotype DX test should not be offered to premenopausal node-positive patients.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e Future clinical trials will shed light on whether ovarian function suppression would replace chemotherapy in these patients. In our study, we could observe endocrine therapy response in low-risk patients and recommend adjuvant chemotherapy in cases of disease progression during NET as a second safety check.\u003c/p\u003e \u003cp\u003eThere are several disadvantages associated with implementing this strategy in clinical practice. We used eight cycles of preoperative anthracyclines and taxanes in our study, and 50% of the patients were found to be lymph node-negative at surgery. A significant proportion of these patients might have been true lymph node-negative before neoadjuvant therapies, as lymph node complete remission is uncommon in ER+/HER2\u0026ndash; population.\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e If we had treated these patients with surgery first rather than neoadjuvant therapy, the lymph node-negative patients would have received four cycles of adjuvant chemotherapy rather than eight cycles. Moreover, 11\u0026middot;9% of patients in our study had pN2 or N3 disease, which is not an indication of using genomic assays according to current guidelines.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe did not find any clinical or presurgical pathological factors significantly associated with BCS conversion. Although not statistically significant due to the small sample size, there were numerical differences according to the four-level classification (subcategories) of MammaPrint index. Among genomic high-risk patients receiving NCT, target size achievement rate and actual BCS rate were higher in H2 patients than in H1 patients, and among genomic low-risk patients receiving NET, UL showed higher BCS conversion than LR. Consistent with our study findings, a previous study showed a significantly higher percentage of pCR in H2 tumours (23%) than in H1 tumours (6\u0026middot;1%) in NCT-treated patients in the NBRST.\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e To the best of our knowledge, our study is the first to show the possibility of better response to NET in UL patients than in LR patients. The target size achievement rate was 100\u0026middot;0% in UL compared with the 52\u0026middot;4% in LR.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, we were unable to recruit the preplanned number of patients due to delay in patient enrolment. Second, our study was not randomised and lacked a control arm; we used historical data as control. This study included only Asian women with relatively small-sized breasts. The general breast conservation rate for early breast cancer in Korea was 68\u0026middot;6% in 2019.\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e The preference for breast conservation is different across countries. In the CALGB 40603 study, only 68% of women who converted from BCS-ineligible to -eligible with neoadjuvant therapy chose breast conservation.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e In contrast, 86\u0026middot;9% of BCS-converted patients with NCT chose BCS in a Korean study.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e In the BrighTNess study, 79\u0026middot;6% of BCS-eligible European and Asian patients chose BCS after neoadjuvant therapy in contrast to 55\u0026middot;0% of North American patients.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e Our strategy might not be highly applicable in North America and other countries where BCS rate is low.\u003c/p\u003e \u003cp\u003eIn conclusion, for women with ER+/HER2\u0026ndash; breast cancer seeking breast preservation but facing challenges with borderline or impossible BCS mainly due to tumour size, our study recommends pre-treatment multigene assays to guide the choice between NCT and NET. This approach significantly increases the chances of achieving BCS while avoiding unnecessary chemotherapy in patients where it is not needed. Our study highlights the feasibility of this strategy in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDr. Han W. had full access to all of the data in the study and take responsibility for the integrity and the accuracy of the data analysis.Concept and design: Han W, Jung JGAcquisition, analysis, or interpretation of data: Han W, Kang E, Jung JG, Kim HK, Lee HB, Kim J, Shin HC, Kim HA, Kim EK, and Son BHDrafting of the manuscript: Han W, Kang ECritical review of the manuscript for important intellectual content: Han W, Son BH, Kim EK, Kim HAStatistical analysis: Kang E, The Medical Research Collaborating Center (MRCC) of Seoul National University Hospital Biomedical Research InstituteObtained funding: Han WAdministrative, technical, or material support: Kang E, Jung JG, Kim HK, Lee HB, Kim J, LEE SB, Park CS, Seong MKSupervision: Son BH, Kim EK, Kim HA\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received financial support and drugs from the following companies: Takeda Pharmaceutical Co., Ltd., Kwang Dong Pharmaceutical Co., Ltd., Shin Poong Pharmaceutical Co., Ltd., HyupJin Corporation, and Agendia, Inc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePresentation:\u0026nbsp;\u003c/strong\u003eThis study was presented as a poster in 2024 ASCO Annual Meeting (Abstract Number: 595).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflict of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHan W and Lee HB are co-founders and members of the DCGen Co., Ltd board of directors. Lee HB received research funding from Devicor Medical Product, Inc. and consulting fees from Need Inc., outside the current work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData deposition and materials sharing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData available: Yes\u003c/p\u003e\n\u003cp\u003eData types: Deidentified participant data\u003c/p\u003e\n\u003cp\u003eHow to access data: A Research Collaboration Proposal Request Form can be submitted to Dr. Wonshik Han ([email protected]) or Byung Ho Son ([email protected]) to be considered for collaboration.\u003c/p\u003e\n\u003cp\u003eWhen available: With publication\u003c/p\u003e\n\u003cp\u003eAdditional Information\u003c/p\u003e\n\u003cp\u003eWho can access the data: Researchers who provide a methodologically sound proposal to achieve aims in the approved proposal\u003c/p\u003e\n\u003cp\u003eTypes of analyses: Specified purposes only\u003c/p\u003e\n\u003cp\u003eMechanisms of data availability: With signed data access agreement.\u003c/p\u003e\n\u003cp\u003eAny additional restrictions: N/A.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFisher B, Bryant J, Wolmark N, Mamounas E, Brown A, Fisher ER, et al. 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Miami; 2024.\u003c/li\u003e\n\u003cli\u003evan Piccart M, van \u0026rsquo;t Veer LJ, Poncet C, Lopes Cardozo JMN, Delaloge S, Pierga JY et al. 70-gene signature as an aid for treatment decisions in early breast cancer: updated results of the phase 3 randomised MINDACT trial with an exploratory analysis by age. \u003cem\u003eLancet Oncol\u003c/em\u003e 2021; \u003cstrong\u003e22\u003c/strong\u003e: 476\u0026ndash;88.\u003c/li\u003e\n\u003cli\u003eKalinsky K, Barlow WE, Gralow JR, Meric-Bernstam F, Albain KS, Hayes DF, et al. 21-gene assay to inform chemotherapy benefit in node-positive breast cancer. \u003cem\u003eN Engl J Med\u003c/em\u003e 2021; \u003cstrong\u003e385\u003c/strong\u003e: 2336\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eAndre F, Ismaila N, Allison KH, Barlow WE, Collyar DE, Damodaran S, et al. 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MammaPrint Index as a predictive biomarker for neoadjuvant chemotherapy response and outcome in patients with HR+HER2- breast cancer in NBRST. \u003cem\u003eJ Clin Oncol\u003c/em\u003e 2023; \u003cstrong\u003e41\u003c/strong\u003e(16_suppl): 521.\u003c/li\u003e\n\u003cli\u003eChoi JE, Kim Z, Park CS, Park EH, Lee SB, Lee SK, et al. Breast cancer statistics in Korea, 2019. \u003cem\u003eJ Breast Cancer\u003c/em\u003e 2023; \u003cstrong\u003e26\u003c/strong\u003e: 207\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eGolshan M, Loibl S, Wong SM, Houber JB, O\u0026rsquo;Shaughnessy J, Rugo HS, et al. Breast conservation after neoadjuvant chemotherapy for triple-negative breast cancer: surgical results from the BrighTNess randomized clinical trial. \u003cem\u003eJAMA Surg\u003c/em\u003e 2020; \u003cstrong\u003e155\u003c/strong\u003e: e195410.\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":"npj-breast-cancer","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"npjbcancer","sideBox":"Learn more about [npj Breast Cancer](http://www.nature.com/npjbcancer/)","snPcode":"41523","submissionUrl":"https://mts-npjbcancer.nature.com/","title":"npj Breast Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"70-gene assay, breast cancer, breast conserving surgery, neoadjuvant chemotherapy, neoadjuvant endocrine therapy","lastPublishedDoi":"10.21203/rs.3.rs-6287262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6287262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe investigated whether tailored neoadjuvant therapy (chemotherapy [NCT] or endocrine therapy [NET]) guided by a 70-gene assay could improve breast-conserving surgery (BCS) rates among patients with ER-positive/HER2-negative breast cancer initially deemed ineligible for BCS. Of 130 prospectively enrolled patients (stage II\u0026ndash;IIIA, across four Korean centers), 92 were analyzed. Patients classified as high genomic risk received NCT, while low-risk patients underwent NET (letrozole\u0026thinsp;\u0026plusmn;\u0026thinsp;leuprolide for premenopausal women) for 16\u0026ndash;24 weeks. The primary endpoint\u0026mdash;achieving the surgeon-defined target tumor size for BCS\u0026mdash;was reached in 69.6% (95% CI: 59.1\u0026ndash;78.7%), significantly surpassing the predefined goal of 50.8% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Actual overall BCS rate was 59.8% (64.7% NCT, 45.8% NET). Pathologic complete response occurred in 2.2%, exclusively in the NCT group. Thus, pretreatment genomic profiling effectively guided therapy selection, substantially increasing BCS eligibility while sparing low-risk patients unnecessary chemotherapy toxicity.\u003c/p\u003e","manuscriptTitle":"Personalized neoadjuvant strategy using 70-gene assay in ER-positive/HER2- negative breast cancer to increase breast-conserving surgery rate (KBCSG016: PLATO trial)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 05:37:24","doi":"10.21203/rs.3.rs-6287262/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-13T13:26:28+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"73806894019227134622185993862875779793","date":"2025-05-07T22:01:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-05T23:13:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197264772073015403506770688939274155039","date":"2025-05-05T22:10:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-21T12:44:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8167112415254490350926382291340944274","date":"2025-04-09T01:26:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230342495089956522654866007482602946076","date":"2025-04-07T02:21:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-03T01:54:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-31T16:40:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-30T18:13:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Breast Cancer","date":"2025-03-23T08:32:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-breast-cancer","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"npjbcancer","sideBox":"Learn more about [npj Breast Cancer](http://www.nature.com/npjbcancer/)","snPcode":"41523","submissionUrl":"https://mts-npjbcancer.nature.com/","title":"npj Breast Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92469a90-6b6f-486b-8155-9e9bbe3f6dc8","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47450172,"name":"Biological sciences/Cancer/Breast cancer"},{"id":47450173,"name":"Biological sciences/Cancer/Cancer genomics"},{"id":47450174,"name":"Biological sciences/Cancer/Tumour biomarkers"},{"id":47450175,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":47450176,"name":"Health sciences/Biomarkers/Prognostic markers"},{"id":47450177,"name":"Health sciences/Oncology/Surgical oncology"}],"tags":[],"updatedAt":"2025-06-23T16:03:11+00:00","versionOfRecord":{"articleIdentity":"rs-6287262","link":"https://doi.org/10.1038/s41523-025-00772-5","journal":{"identity":"npj-breast-cancer","isVorOnly":false,"title":"npj Breast Cancer"},"publishedOn":"2025-06-20 15:57:56","publishedOnDateReadable":"June 20th, 2025"},"versionCreatedAt":"2025-05-05 05:37:24","video":"","vorDoi":"10.1038/s41523-025-00772-5","vorDoiUrl":"https://doi.org/10.1038/s41523-025-00772-5","workflowStages":[]},"version":"v1","identity":"rs-6287262","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6287262","identity":"rs-6287262","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}