Delayed and Selective Bone Health Assessment Following Cancer Diagnosis: A Real-World Gap in Survivorship Care

Delayed and Selective Bone Health Assessment Following Cancer Diagnosis: A Real-World Gap in Survivorship Care | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Delayed and Selective Bone Health Assessment Following Cancer Diagnosis: A Real-World Gap in Survivorship Care Vanessa Rouach, Ziv Versano, Meital Sasson, Yona Greenman, Arnon Makori This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8955916/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background : Cancer treatment–induced bone loss (CTIBL) is well recognized in hormone-sensitive malignancies such as breast and prostate cancer but is less consistently implemented in patients with other solid tumors and hematologic malignancies. Furthermore, the long-term skeletal effects of newer systemic therapies, including immune checkpoint inhibitors and targeted agents, remain incompletely characterized. Although bone mineral density (BMD) assessment by dual-energy X-ray absorptiometry (DXA) is recommended for patients at increased skeletal risk, real-world utilization patterns remain poorly characterized. Objectives: To evaluate DXA utilization, timing, BMD results, and associated clinical characteristics following a new cancer diagnosis in a large real-world cohort. Methods: We conducted a retrospective cohort study using the MDClone platform, including adults with a first cancer diagnosis between January 1, 2015, and December 31, 2024, treated at Assuta Medical Centers in Israel. DXA completion, timing, BMD and T-scores, and prevalence of osteopenia and osteoporosis were analyzed. Results: Results: Among 21,112 patients with newly diagnosed cancer (77.0% women; mean age 60.2 ± 13.1 years), 7,496 (35.5%) underwent DXA at any time following diagnosis. DXA utilization was significantly lower in men than in women (15.0% vs. 41.7%). Early DXA assessment within 6 months of diagnosis was uncommon (1,550 patients; 7.3% of the total cohort), and only 14.1% underwent DXA within the first 12 months. Median time from cancer diagnosis to DXA was 14.8 months. DXA utilization varied by cancer type and was highest in breast cancer (39.7%), compared with prostate cancer (27.6%) and other malignancies (27.8%). Among patients undergoing DXA, 40.2% had normal BMD, 45.2% had osteopenia, and 14.5% met criteria for osteoporosis. Conclusions : In our real-world experience within a large private healthcare network, DXA assessment after cancer diagnosis is infrequently performed and often delayed, with marked disparities by sex and cancer type. A substantial burden of low bone mass is already present at the time of evaluation, highlighting missed opportunities for early skeletal risk identification. cancer treatment-induced bone loss (CTIBL) Dual X-Ray Absorptiometry (DXA) osteoporosis osteopenia bone mineral density fracture prevention Figures Figure 1 Figure 2 1. Introduction Population aging has led to a substantial rise in cancer incidence worldwide, with older adults accounting for most new diagnoses and cancer-related mortality. Advances in early detection and oncologic therapies have significantly improved survival, resulting in a rapidly growing population of cancer survivors. As malignancy increasingly becomes a chronic condition, survivorship care must extend beyond tumor control to address long-term treatment-related complications, including skeletal health ( 1 , 2 ). Skeletal morbidity represents a major but often under-recognized component of cancer survivorship. Osteoporosis and fragility fractures are common age-related conditions, and their risk is further amplified in patients with cancer due to both the malignant process itself and cancer-directed therapies ( 3 – 6 ). Cancer-treatment induced bone loss (CTIBL) is a multifactorial phenomenon driven by systemic inflammation, reduced physical activity, nutritional deficiencies, and exposure to bone-depleting treatments such as chemotherapy, radiotherapy, endocrine therapies, glucocorticoids, and hematopoietic stem-cell transplantation ( 6 – 10 ). These mechanisms may act synergistically to accelerate bone turnover, reduce bone mineral density (BMD), and increase fracture risk. Bone health complications are well established in hormone-sensitive malignancies, particularly breast and prostate cancer, where aromatase inhibitors and androgen deprivation therapy are strongly associated with accelerated bone loss and increased fracture risk ( 11 – 18 ). Consequently, clinical awareness and guideline recommendations regarding bone health assessment are more clearly defined in these populations. However, accumulating evidence indicates that skeletal fragility is not confined to hormone-sensitive cancers. Population-based studies have demonstrated an increased fracture risk across a broad range of malignancies, with risk persisting for years after cancer diagnosis, underscoring the broader relevance of bone health in oncology ( 19 – 22 ). Despite this elevated risk, osteoporosis risk assessment tools frequently underestimate fracture risk in cancer survivors, and a history of malignancy is not consistently incorporated into fracture prediction models ( 21 ). International clinical practice guidelines from oncology and endocrine societies, including the European Society for Medical Oncology, the American Society of Clinical Oncology, and the Endocrine Society, recommend bone health evaluation using dual-energy X-ray absorptiometry (DXA) in patients at increased skeletal risk, particularly those exposed to bone-toxic therapies ( 23 – 26 ). Nevertheless, real-world studies consistently demonstrate low rates of DXA utilization and osteoporosis management among oncology patients, even within well-defined high-risk groups ( 27 – 31 ). Data describing bone health assessment patterns across unselected cancer populations in routine care remain limited. In particular, there is a paucity of large real-world analyses evaluating the timing and uptake of DXA assessment following cancer diagnosis, as well as the extent to which bone loss is already present at the time of evaluation. Understanding these patterns is essential for identifying gaps in care and informing strategies for more integrated bone–oncology and survivorship pathways. The present study aimed to evaluate DXA utilization following a new cancer diagnosis within a large real-world oncology cohort treated in a private healthcare network in Israel. Specifically, we examined the frequency and timing of DXA assessment, BMD outcomes, and patient characteristics associated with DXA referral, in order to identify disparities in bone health evaluation and highlight opportunities for earlier skeletal risk identification. 2. Methods 2.1 Study Design and Data Source We conducted a retrospective cohort study using the MDClone platform, which allows generation of fully anonymized yet statistically accurate datasets. Adults (≥ 18 years) with a first documented cancer diagnosis in Assuta Medical Centers between January 1, 2015, and December 31, 2024 were included. Assuta Medical Centers is a leading private healthcare provider in Israel, operating four hospitals and multiple diagnostic centers across the country. The network provides a broad range of medical services, including oncology, surgery, and advanced imaging, and serves over two million patients annually. Assuta is owned by Maccabi Healthcare Services, one of Israel’s largest health maintenance organizations, and treats patients through both insured and private frameworks. The study cohort therefore represents real-world care patterns within a large private healthcare network with broad national reach. 2.2 Population and Inclusion Criteria Patients were eligible if they were aged 18 years or older, had a new diagnosis of any malignancy during the study period, and had complete demographic data. Patients with missing essential demographic information were excluded. 2.3 Data Collected For all subjects with a newly diagnosed malignancy treated in Assuta Medical Centers, we collected demographics age, sex, BMI, clinical characteristics: smoking status, cancer type, and the treatment received. Cancer diagnosis and site were defined according to the recorded organ of origin at the time of diagnosis, based on structured electronic medical record documentation. Data on oncologic treatments were extracted from structured electronic medical records using the MDClone platform and included documented exposure to chemotherapy, endocrine therapy, biologic agents, radiotherapy, and surgical procedures. Bone mineral density was assessed by dual-energy X-ray absorptiometry (DXA), performed using GE Lunar DXA (GE Healthcare, Madison, WI, USA) systems. We extracted DXA parameters: lumbar spine, total hip, and femoral neck BMD and T-scores. We calculated the time from cancer diagnosis to DXA completion, and defined bone status categories: osteoporosis (T-score ≤ − 2.5) and osteopenia (T-score between − 1.0 and − 2.5 according to WHO criteria using the lowest T-score obtained from lumbar spine, femoral neck, or total hip measurements. 2.4 Outcomes Primary outcome: Proportion of cancer patients undergoing DXA. Secondary outcomes were the time between the oncologic diagnosis and DXA scan performance, BMD/T-score distribution, prevalence of osteoporosis and osteopenia, and the distribution of DXA testing by cancer type. 2.5 Statistical Analysis Continuous variables were summarized as mean ± standard deviation, and categorical variables as counts and percentages. Comparisons between patients who underwent DXA assessment and those who did not were performed using Student’s t -test or Mann–Whitney U test for continuous variables, as appropriate, and χ² test for categorical variables. Time from cancer diagnosis to DXA assessment was evaluated descriptively, and cumulative incidence curves were used to illustrate differences in timing according to sex. Multivariable logistic regression analysis was performed to identify factors independently associated with DXA assessment following cancer diagnosis. Covariates included age, sex, cancer type (grouped by hormone sensitivity), and documented exposure to chemotherapy and radiotherapy. Results are presented as adjusted odds ratios with 95% confidence intervals. All analyses were descriptive and exploratory in nature, aimed at characterizing patterns of bone health assessment and identifying disparities in care within the oncology population. Statistical analyses were conducted using IBM SPSS Statistics software (version 29), and a two-sided p value < 0.05 was considered statistically significant. 3. Results 3.1. Study Population The study included 21,112 adults with a first diagnosis of malignancy between 01.01.2015 and 31.12.2024. Women comprised 77.0% of the cohort (n = 16,244), while 22.9% were men (n = 4,838). The mean age of the cohort was 60.2 ± 13.1 years, and mean BMI was 26.7 ± 5.4 kg/m². Documented current or former smoking was present in 11,059 patients (52.4%). Documented chemotherapy exposure was recorded in 4,512 patients (21.4%) and radiotherapy in 1,473 (7.0%) (Table 1 .). Table 1 Baseline Characteristics of the Study Cohort Characteristic Total cohort (n = 21,112) Age, years 60.2 ± 13.1 Female sex, n (%) 16,244 (77.0%) Body mass index (BMI), kg/m² 26.7 ± 5.4 Current or former smoking†, n (%) 11,059 (52.4%) Breast cancer, n (%) 8,851 (41.9%) Prostate cancer, n (%) 1,411 (6.7%) Other malignancies, n (%) 10,850 (51.4%) DXA performed after cancer diagnosis, n (%) 7,496 (35.5%) DXA within 6 months of diagnosis, n (%) 1,550 (7.3%) Documented chemotherapy exposure†, n (%) 4,512 (21.4%) Documented radiotherapy exposure†, n (%) 1,473 (7.0%) † As documented in the medical record. 3.2. Utilization of Bone Density Assessment Overall, 7,496 patients (35.5%) underwent DXA assessment following cancer diagnosis, of whom 1,550 (7.3%) were evaluated within 6 months of diagnosis. Thus, nearly two-thirds of oncology patients did not receive objective skeletal evaluation, despite known cancer- and treatment-related bone risks. Patients who underwent DXA differed significantly from those who did not, being older, more frequently female, and more likely to have hormone-sensitive malignancies. Documented smoking status and oncologic treatment exposures were substantially more frequent among DXA performers (all p < 0.001) possibly reflecting more comprehensive clinical documentation rather than true differences in exposure prevalence. (Table 2 ). Table 2 Characteristics of Patients With and Without DXA Assessment Characteristic DXA performed (n = 7,496) No DXA performed (n = 13,616) p value* Age, years 61.6 ± 11.2 59.4 ± 13.9 < 0.001 Female sex, n (%) 6,771 (90.3%) 9,473 (69.6%) < 0.001 BMI, kg/m² 27.0 ± 5.3 26.4 ± 5.4 < 0.001 Current or former smoking†, n (%) 6,372 (85.0%) 4,687 (34.4%) < 0.001 Documented chemotherapy exposure†, n (%) 4,512 (60.2%) 1,906 (14.0%) < 0.001 Documented radiotherapy exposure†, n (%) 1,473 (19.6%) 681 (5.0%) < 0.001 Breast cancer, n (%) 3,782 (49.5%) 5,067 (37.2%) < 0.001 Prostate cancer, n (%) 440 (5.8%) 971 (7.1%) < 0.01 p values derived from χ² tests for categorical variables and Student’s t-test or Mann– Whitney U test for continuous variables, as appropriate. † Smoking status and oncologic treatment exposure were incompletely captured in the electronic health record and are reported as documented exposure only. 3.3 Sex Disparities in DXA Assessment DXA utilization differed markedly by sex. Women were significantly more likely to undergo DXA than men (41.7% vs. 15.0%). Male sex was associated with a substantially lower likelihood of DXA assessment. 3.4. DXA Assessment by Cancer Type and treatment DXA utilization varied significantly by cancer type. The highest rates were observed in breast cancer (42.2%) and prostate cancer (30.0%), accounting for approximately half of all DXA examinations. In contrast, DXA assessment was infrequent in lung, gastrointestinal, ovarian, and pancreatic cancers, with utilization rates generally below 15%. Using an organ-based classification, hematologic malignancies accounted for approximately 6–8% of the overall cohort and 4–5% of patients who underwent DXA assessment (Fig. 1.). Among patients who underwent DXA, 4,512 (≈ 60%) had documented exposure to chemotherapy and 1,473 (≈ 20%) had received radiotherapy. Figure 1. Proportion of patients undergoing DXA assessment following cancer diagnosis, stratified by cancer type. * Hematologic malignancies were identified using an organ-based classification and may be under-ascertained. 3.5. Timing of DXA Assessment Early skeletal evaluation was uncommon. Only 1,550 patients (7.3%) underwent DXA within 6 months of cancer diagnosis. Among those who underwent DXA, the median time to assessment was 14.8 months, indicating that bone health evaluation was generally delayed and not integrated into early oncologic care. Early DXA assessment within 6 months of diagnosis was uncommon (1,550 patients; 7.3% of the total cohort), and only 14.1% underwent DXA within the first 12 months. Although women were significantly more likely than men to undergo DXA, timing among those evaluated was broadly comparable between sexes (Fig. 2A). Median time from cancer diagnosis to DXA differed by malignancy type. Among breast cancer patients (n = 3,516), median time to DXA was 12.9 months (IQR 5.8–28.0). In prostate cancer (n = 390), median time was 12.7 months (IQR 4.4–28.8). In contrast, patients with other malignancies (n = 3,019) experienced a substantially longer delay, with a median time of 17.9 months (IQR 8.3–35.1) (Fig. 2B). Patients without DXA were administratively censored at the end of the study period. Figure 2A. Cumulative distribution of time to DXA assessment after cancer diagnosis, stratified by sex. Curves represent the cumulative proportion of patients undergoing DXA over time among those who received bone density assessment. 2B. Cumulative distribution of time to DXA assessment after cancer diagnosis stratified by cancer type. Curves represent the cumulative proportion of patients undergoing DXA over time among DXA performers. 3.6. Bone Density Findings Among patients who underwent DXA assessment (n = 5,570 with complete evaluable measurements), bone mineral density at the time of evaluation was frequently already abnormal. Using the lowest T-score across lumbar spine, femoral neck, and total hip, 2,520 patients (45.2%) met criteria for osteopenia and 809 (14.5%) met criteria for osteoporosis, whereas only 2,241 (40.2%) had normal BMD. Among patients with osteopenia, the mean lowest T-score was − 1.72 ± 0.39, suggesting established low bone mass rather than marginal diagnostic values. In those meeting criteria for osteoporosis, the mean lowest T-score was − 2.95 ± 0.47, indicating clinically significant skeletal compromise well below the diagnostic threshold (Table 3 ). At cortical skeletal sites, bone loss was particularly evident. Among patients classified as osteoporotic, femoral neck values frequently approached or exceeded the threshold associated with increased hip fracture risk. These findings suggest that DXA assessment was often performed after substantial bone loss had already occurred. Table 3 Bone Mineral Density T-Score Results among DXA Performers (n = 5,570*) Diagnostic category (lowest T-score) n % Mean lowest T-score ± SD Normal (T ≥ − 1.0) 2,241 40.2% −0.16 ± 0.71 Osteopenia (− 1.0 > T > − 2.5) 2,520 45.2% −1.72 ± 0.39 Osteoporosis (T ≤ − 2.5) 809 14.5% −2.95 ± 0.47 *Includes patients with complete evaluable DXA measurements across skeletal sites. In a multivariable logistic regression analysis, female sex was independently associated with higher odds of undergoing DXA assessment compared with male sex (adjusted OR 2.13, 95% CI 1.79–2.50; p < 0.001). Increasing age was also associated with greater odds of DXA assessment (aOR per year 1.03, 95% CI 1.03–1.03; p < 0.001). Patients with breast cancer and prostate cancer had markedly higher odds of DXA assessment compared with patients with other cancer types (aOR 5.82 and 5.29, respectively; both p < 0.001). Collinearity between covariates was assessed and no clinically meaningful multi-collinearity was identified (Table 4 .). Table 4 Multivariable Logistic Regression Analysis of Factors Associated With DXA Performance Predictor Adjusted OR (aOR) 95% CI p value Female sex (vs male) 2.13 1.79–2.50 < 0.001 Age (per year) 1.03 1.03–1.03 < 0.001 Breast cancer (vs other cancers) 5.82 5.15–6.58 < 0.001 Prostate cancer (vs other cancers) 5.29 4.42–6.33 < 0.001 4. Discussion In this large real-world oncology cohort, bone mineral density assessment following a new cancer diagnosis was markedly underutilized and frequently delayed. Fewer than 40% of patients underwent DXA at any time after diagnosis, and only a small minority were evaluated within the first 6 months. Despite low screening rates, nearly two-thirds of patients who underwent DXA had abnormal bone density, and more than one in six met criteria for osteoporosis. These findings highlight a substantial gap between guideline recommendations and routine clinical practice, underscoring the need for improved integration of bone health assessment into oncology care pathways. Importantly, the DXA-performing cohort was heavily enriched with breast cancer patients, who accounted for approximately 42% of all bone density assessments. Given that breast cancer represents a population with well-established guideline recommendations for skeletal monitoring, one might expect high rates of early DXA integration. Nevertheless, overall DXA utilization in breast cancer remained below 50%, and early assessment within six months of diagnosis was uncommon. These findings suggest that gaps in bone health evaluation persist even within guideline-targeted populations and are not confined to non–hormone-sensitive malignancies. Although breast cancer comprised the largest single cancer type, more than half of the cohort (51.4%) consisted of non–breast and non–prostate malignancies, providing a broad representation of solid and hematologic cancers in routine practice. In this group, DXA use was substantially lower, despite well-established skeletal risks related to systemic therapy, corticosteroids, and transplantation. This gap is particularly relevant in the modern therapeutic era. The skeletal effects of newer systemic therapies—including immune checkpoint inhibitors and targeted agents—remain incompletely characterized. Emerging mechanistic and observational data suggest that immune modulation may influence bone remodeling pathways; however, longitudinal bone mineral density and fracture outcomes remain insufficiently defined ( 32 – 33 ). In a recent meta-analysis of randomized trials involving over 19,000 patients, immune checkpoint inhibitor use was associated with a non-significant 18% increase in clinical fracture incidence compared with controls (OR 1.18, 95% CI 0.82–1.70), although fracture events were infrequently reported and estimates were imprecise ( 34 ). Together, these findings highlight ongoing uncertainty regarding long-term skeletal risk in the era of immune modulation. In this evolving therapeutic landscape, reliance on traditional risk frameworks may underestimate bone vulnerability across contemporary cancer care. Sex-based disparities were also evident. Women were more likely to undergo DXA assessment. While this may partly reflect the predominance of breast cancer and greater familiarity with osteoporosis screening in women, it raises concern that men with cancer—particularly those receiving bone-depleting therapies—remain underserved with respect to bone health evaluation. The delayed timing of DXA observed in this cohort further emphasizes missed opportunities for early intervention. Bone loss associated with cancer therapies often occurs early in the treatment course, and timely identification of low bone mass allows for preventive strategies that may reduce fracture risk and preserve functional independence. This study has several limitations. Its retrospective design and reliance on electronic health record data introduce the potential for incomplete or inconsistent documentation, particularly for treatment exposures and lifestyle factors. Information on cancer histology, disease stage, and treatment dose or duration—factors that may influence skeletal outcomes—was not uniformly available and could not be fully incorporated into the analysis. Hematologic malignancies and stem-cell transplantation could not be systematically identified, precluding detailed subgroup analyses in these high-risk populations. DXA examinations performed outside Assuta Medical Centers could not be captured in the present dataset. However, given that bone density testing is routinely reimbursed and widely available within the network, performance of DXA at external facilities is considered unlikely, and any resulting under-ascertainment is expected to be limited. Additionally, DXA performance was used as a surrogate for bone health evaluation, and fracture outcomes were not assessed. Nonetheless, the large cohort size and real-world setting provide valuable insight into current practice patterns and highlight clinically relevant gaps in care. Importantly, more than half of the cohort consisted of non–breast and non–prostate malignancies, ensuring broad representation beyond traditionally studied hormone-sensitive cancers. In summary, our real-world experience reveals that bone density assessment following cancer diagnosis is often delayed and selectively applied, despite a high prevalence of low bone mass at the time of evaluation. These observations highlight missed opportunities for early skeletal risk identification and support more proactive incorporation of bone health assessment into routine oncology and survivorship care. 4. Conclusions In conclusion, in our real-world experience, bone density assessment following cancer diagnosis was infrequently performed and commonly delayed, with substantial disparities according to sex and cancer type. These observations should be interpreted in the context of current guideline recommendations, which primarily target patients in hormone-sensitive cancers receiving endocrine therapies or those with additional fracture risk factors. Nevertheless, the high prevalence of low bone mass among patients who underwent DXA suggests that skeletal risk is often recognized only after clinically relevant bone loss has already occurred. Collectively, these findings support the need for more systematic and proactive integration of bone health evaluation into oncology and survivorship care, including populations beyond traditionally recognized high-risk malignancies. Declarations Ethics Approval and Consent to Participate The study was reviewed by the Assuta Medical Centers Institutional Review Board and was granted exemption from formal ethical approval due to the use of fully anonymized retrospective data. Informed consent was waived in accordance with institutional and national guidelines. Funding This research received no external funding. Conflicts of Interest The authors declare no conflicts of interest. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work the author (Vanessa Rouach) used ChatGPT (OpenAI) to assist with language editing and refinement of the text. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the published article. References Brennan M, Kalsi T. Management of bone health considerations in patients with cancer. Cancers (Basel). 2025;17:2878. doi:10.3390/cancers17172878. Link-Rachner CS, Göbel A, Jaschke NP, Rachner TD. Endocrine health in survivors of adult-onset cancer. Lancet Diabetes Endocrinol. 2024;12:350–364. doi:10.1016/S2213-8587(24)00088-3 Reid DM, Doughty J, Eastell R, Heys SD, Howell A, McCloskey EV, Powles T, Selby P, Coleman RE. Guidance for the management of breast cancer treatment-induced bone loss: a consensus position statement from a UK Expert Group. Cancer Treat Rev. 2008;34 Suppl 1:S3-18. doi: 10.1016/j.ctrv.2008.03.007. Epub 2008 Jun 2. PMID: 18515009. Aapro M, Abrahamsson PA, Body JJ, Coleman RE, Colomer R, Costa L, Crinò L, Dirix L, Gnant M, Gralow J, Hadji P, Hortobagyi GN, Jonat W, Lipton A, Monnier A, Paterson AH, Rizzoli R, Saad F, Thürlimann B. Guidance on the use of bisphosphonates in solid tumours: recommendations of an international expert panel. Ann Oncol. 2008 Mar;19(3):420-32. doi: 10.1093/annonc/mdm442. Epub 2007 Sep 28. PMID: 17906299. Waqas K, Lima Ferreira J, Tsourdi E, Body JJ, Hadji P, Zillikens MC. Updated guidance on the management of cancer treatment-induced bone loss (CTIBL) in pre- and postmenopausal women with early-stage breast cancer. J Bone Oncol. 2021 Mar 18;28:100355. doi: 10.1016/j.jbo.2021.100355. PMID: 33948427; PMCID: PMC8080519. Rayner-Myers SD, Hunter K, Pituskin E. Direct and Indirect Mechanisms of Chemotherapy-Induced Bone Loss in Adjuvant Breast Cancer: An Integrative Review. Semin Oncol Nurs. 2022 Apr;38(2):151280. doi: 10.1016/j.soncn.2022.151280. Epub 2022 Apr 25. PMID: 35477650. Pacheco R, Stock H. Effects of radiation on bone. Curr Osteoporos Rep. 2013 Dec;11(4):299-304. doi: 10.1007/s11914-013-0174-z. PMID: 24057133. Donaubauer AJ, Deloch L, Becker I, Fietkau R, Frey B, Gaipl US. The Influence of Radiation on Bone and Bone Cells-Differential Effects on Osteoclasts and Osteoblasts. Int J Mol Sci. 2020 Sep 2;21(17):6377. doi: 10.3390/ijms21176377. PMID: 32887421; PMCID: PMC7504528. Axelsen CT, Jensen AB, Jakobsen EH, Bechmann T. Bone loss during neoadjuvant/adjuvant chemotherapy for early-stage breast cancer. Mol Clin Oncol. 2018;8:767–772. doi:10.3892/mco.2018.1615. Bouvard B, Soulat G, Hélis V, et al. Bone loss and fracture risk after chemotherapy for breast cancer. Osteoporos Int. 2021;32:1683–1691. doi:10.1007/s00198-021-05914-z. Hadji P, Aapro M, Body JJ, et al. Management of aromatase inhibitor–associated bone loss. Lancet Oncol. 2017;18:e319–e328. doi:10.1016/S1470-2045(17)30036-5. Saad F, Adachi JD, Brown JP, Canning LA, Gelmon KA, Josse RG, Pritchard KI. Cancer treatment-induced bone loss in breast and prostate cancer. J Clin Oncol. 2008 Nov 20;26(33):5465-76. doi: 10.1200/JCO.2008.18.4184. Epub 2008 Oct 27. PMID: 18955443. Hadji P, Aapro MS, Body JJ, Bundred NJ, Brufsky A, Coleman RE, Gnant M, Guise T, Lipton A. Management of aromatase inhibitor-associated bone loss in postmenopausal women with breast cancer: practical guidance for prevention and treatment. Ann Oncol. 2011 Dec;22(12):2546-2555. doi: 10.1093/annonc/mdr017. Epub 2011 Mar 17. PMID: 21415233. Khan MI. Management of bone loss due to endocrine therapy during cancer treatment. Osteoporos Int. 2023;34:671–680. doi:10.1007/s00198-023-06672-3. Takahashi S. Management of cancer treatment–induced bone loss in breast and prostate cancer. J Bone Miner Metab. 2023;41:307–316. doi:10.1007/s00774-023-01414-1. Liang I, Brennan S, Girgis C, Hayden A, Moujaber T, Turner S, Vasista A, Grossmann M, Wong PKK. Bone Health Management in Men Commencing Androgen Deprivation Therapy for Prostate Cancer and Women Commencing Anti-Oestrogen Therapy for Breast Cancer. Cancer Med. 2025 May;14(9):e70873. doi: 10.1002/cam4.70873. PMID: 40357899; PMCID: PMC12070255. Brown JE, Handforth C, Compston JE, Cross W, Parr N, Selby P, Wood S, Drudge-Coates L, Walsh JS, Mitchell C, Collinson FJ, Coleman RE, James N, Francis R, Reid DM, McCloskey E. Guidance for the assessment and management of prostate cancer treatment-induced bone loss. A consensus position statement from an expert group. J Bone Oncol. 2020 Aug 2;25:100311. doi: 10.1016/j.jbo.2020.100311. PMID: 32995252; PMCID: PMC7516275. Jones C, Gray S, Brown M, Brown J, McCloskey E, Rai BP, Clarke N, Sachdeva A. Risk of Fractures and Falls in Men with Advanced or Metastatic Prostate Cancer Receiving Androgen Deprivation Therapy and Treated with Novel Androgen Receptor Signalling Inhibitors: A Systematic Review and Meta-analysis of Randomised Controlled Trials. Eur Urol Oncol. 2024 Oct;7(5):993-1004. doi: 10.1016/j.euo.2024.01.016. Epub 2024 Feb 20. PMID: 38383277. Teissonnière M, Point M, Biver E, Hadji P, Bonnelye E, Ebeling PR, Kendler D, de Villiers T, Holzer G, Body JJ, Fuleihan GEH, Brandi ML, Rizzoli R, Confavreux CB. Bone Effects of Anti-Cancer Treatments in 2024. Calcif Tissue Int. 2025 Mar 27;116(1):54. doi: 10.1007/s00223-025-01362-0. PMID: 40146323; PMCID: PMC11950069. Rizzoli R, Body JJ, Brandi ML, Cannata-Andia J, Chappard D, El Maghraoui A, Glüer CC, Kendler D, Napoli N, Papaioannou A, Pierroz DD, Rahme M, Van Poznak CH, de Villiers TJ, El Hajj Fuleihan G; International Osteoporosis Foundation Committee of Scientific Advisors Working Group on Cancer-Induced Bone Disease. Cancer-associated bone disease. Osteoporos Int. 2013 Dec;24(12):2929-53. doi: 10.1007/s00198-013-2530-3. Epub 2013 Oct 22. PMID: 24146095; PMCID: PMC5104551. Ye C, Leslie WD. Fracture risk and assessment in adults with cancer. Osteoporos Int. 2023 Mar;34(3):449-466. doi: 10.1007/s00198-022-06631-4. Epub 2022 Dec 13. PMID: 36512057. Leslie WD, Edwards B, Al-Azazi S, Yan L, Lix LM, Czaykowski P, Singh H. Cancer patients with fractures are rarely assessed or treated for osteoporosis: a population-based study. Osteoporos Int. 2021 Feb;32(2):333-341. doi: 10.1007/s00198-020-05596-6. Epub 2020 Aug 17. PMID: 32808139 Coleman R, Hadji P, Body JJ, et al. Bone health in cancer: ESMO Clinical Practice Guidelines. Ann Oncol. 2020;31:1650–1663. doi:10.1016/j.annonc.2020.07.019. Shapiro CL, Van Poznak C, Lacchetti C, et al. Management of osteoporosis in survivors of adult cancers. J Clin Oncol. 2019;37:2916–2946. doi:10.1200/JCO.19.01696. 25. Rizzoli R, Body JJ, Brandi ML, Cannata-Andia J, Chappard D, El Maghraoui A, Glüer CC, Kendler D, Napoli N, Papaioannou A, Pierroz DD, Rahme M, Van Poznak CH, de Villiers TJ, El Hajj Fuleihan G; International Osteoporosis Foundation Committee of Scientific Advisors Working Group on Cancer-Induced Bone Disease. Cancer-associated bone disease. Osteoporos Int. 2013 Dec;24(12):2929-53. doi: 10.1007/s00198-013-2530-3. Epub 2013 Oct 22. PMID: 24146095; PMCID: PMC5104551. 26. Eastell R, Rosen CJ, Black DM, Cheung AM, Murad MH, Shoback D. Pharmacological management of osteoporosis in postmenopausal women: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2019;104(5):1595–1622. doi:10.1210/jc.2019-00221 Tseng OL, Dawes MG, Spinelli JJ, Gotay CC, McBride ML. Utilization of bone mineral density testing among breast cancer survivors in British Columbia, Canada. Osteoporos Int. 2017 Dec;28(12):3439-3449. doi: 10.1007/s00198-017-4218-6. Epub 2017 Oct 9. PMID: 28993862. Stratton J, Hu X, Soulos PR, Davidoff AJ, Pusztai L, Gross CP, Mougalian SS. Bone Density Screening in Postmenopausal Women With Early-Stage Breast Cancer Treated With Aromatase Inhibitors. J Oncol Pract. 2017 May;13(5):e505-e515. doi: 10.1200/JOP.2016.018341. Epub 2017 Mar 7. PMID: 28267392. Bailey S, Lin J. The association of osteoporosis knowledge and beliefs with preventive behaviors in postmenopausal breast cancer survivors. BMC Womens Health. 2021 Aug 11;21(1):297. doi: 10.1186/s12905-021-01430-1. PMID: 34380488; PMCID: PMC8359538. Rouach V, Greenman Y, Chodick G, Goldshtein I. DXA assessment and fracture prevention after aromatase inhibitor initiation. J Bone Oncol. 2023;42:100501. doi:10.1016/j.jbo.2023.100501. Harmouch SS, Berger AJ, Cole AP. Low rates of bone density testing in prostate cancer survivors on androgen-deprivation therapy: where do we go from here? BJU Int. 2018 Apr;121(4):492-493. doi: 10.1111/bju.14120. PMID: 29603897. Wang JW, Dai MW, Liu JH. The regulatory effects of PD-1/PD-L1 inhibitors on bone metabolism: opportunities and challenges in osteoporosis management. Front Immunol. 2025 Jul 25;16:1630751. doi: 10.3389/fimmu.2025.1630751. PMID: 40787445; PMCID: PMC12331705. Siampanopoulou V, Ziogas DC, Lyrarakis G, Anastasopoulou A, Kassi E, Gogas H, Angelousi A. Bone mineral density changes following immune checkpoint inhibitor therapy: insights from a case series analysis. Osteoporos Int. 2025 Sep;36(9):1711-1718. doi: 10.1007/s00198-025-07647-2. Epub 2025 Aug 10. PMID: 40783905. Yavropoulou MP, Anastasilakis DA, Kasdagli MI, Gialouri CG, Palaiopanos K, Fountas A, Anastasilakis AD, Daskalakis K, Dekkers OM, Lems WF, Papapoulos SE, Makras P. Incidence of fractures in patients with solid cancers treated with immune checkpoint inhibitors: a systematic review and meta-analysis of randomised controlled trials. BMJ Oncol. 2025 Nov 24;4(1):e000868. doi: 10.1136/bmjonc-2025-000868. PMID: 41323423; PMCID: PMC12658493. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 31 Mar, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 24 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8955916","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616811543,"identity":"ba402ef1-c49e-4a44-971e-1399d9d61c2a","order_by":0,"name":"Vanessa Rouach","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie3RsarCMBSA4RMKnZSsqX2JcwlUBKmvUuma630ABVOEOLrqizhXCumiu5teXB10q9BBJykOad0E828H8nEOBMBm++QoOPI5pMcmxFOkQqImBHWFgIlgvtv8F2X4xw+J6uwnEC5YRIxbcDuKeVvFvbUmyhca4tUyMh/mSRH4RDoYaDLjvxJi3KY1ZHHu3opyilw1JZSJAFpuhuiS5PQgIeayjpy531Y5Mj1MTkKzyJsn0khcKn6uRTlGOsvSTEz6A+o42eViIK+xoYTqBzVp8N5zm81m+4buh05Mz8yZ0CAAAAAASUVORK5CYII=","orcid":"","institution":"Tel Aviv Sourasky Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Vanessa","middleName":"","lastName":"Rouach","suffix":""},{"id":616811544,"identity":"cac379a6-8646-4b6c-8071-e7c75f8e4c80","order_by":1,"name":"Ziv Versano","email":"","orcid":"","institution":"Assuta Medical Centers","correspondingAuthor":false,"prefix":"","firstName":"Ziv","middleName":"","lastName":"Versano","suffix":""},{"id":616811545,"identity":"f2aff672-63fe-4108-8f32-49315702c17b","order_by":2,"name":"Meital Sasson","email":"","orcid":"","institution":"Assuta Medical Centers","correspondingAuthor":false,"prefix":"","firstName":"Meital","middleName":"","lastName":"Sasson","suffix":""},{"id":616811546,"identity":"435475a5-b08d-4e7b-b990-e0baea91a9bb","order_by":3,"name":"Yona Greenman","email":"","orcid":"","institution":"Tel Aviv Sourasky Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Yona","middleName":"","lastName":"Greenman","suffix":""},{"id":616811547,"identity":"71bd3b36-9b78-4ac9-bd78-07afea5f2fd6","order_by":4,"name":"Arnon Makori","email":"","orcid":"","institution":"Assuta Medical Centers","correspondingAuthor":false,"prefix":"","firstName":"Arnon","middleName":"","lastName":"Makori","suffix":""}],"badges":[],"createdAt":"2026-02-24 10:10:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8955916/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8955916/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106300658,"identity":"8671a794-61dc-4373-9b18-b0c2d822c4ea","added_by":"auto","created_at":"2026-04-07 09:15:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210828,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eProportion of patients undergoing DXA assessment following cancer diagnosis, stratified by cancer type.\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003eHematologic malignancies were identified using an organ-based classification and may be under-ascertained.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1supportivecare.png","url":"https://assets-eu.researchsquare.com/files/rs-8955916/v1/2c418fb6ac1b6789857318f7.png"},{"id":106403770,"identity":"bbe38217-4725-4f9d-a000-2fed1cf83a12","added_by":"auto","created_at":"2026-04-08 09:14:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8685874,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eA. Cumulative distribution of time to DXA assessment after cancer diagnosis, stratified by sex.\u003c/u\u003e Curves represent the cumulative proportion of patients undergoing DXA over time among those who received bone density assessment.\u003cu\u003e B. Cumulative distribution of time to DXA assessment after cancer diagnosis stratified by cancer type.\u003c/u\u003e Curves represent the cumulative proportion of patients undergoing DXA over time among DXA performers.\u003c/p\u003e","description":"","filename":"FIGURE2AB24.2.2026.png","url":"https://assets-eu.researchsquare.com/files/rs-8955916/v1/cc2234da5cc2cbbe1f62b246.png"},{"id":106415121,"identity":"26b8087e-0b10-4345-8154-5a60773d2335","added_by":"auto","created_at":"2026-04-08 10:33:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9604806,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8955916/v1/6fb248a1-48f2-4fa1-931b-7507c6b54b7b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Delayed and Selective Bone Health Assessment Following Cancer Diagnosis: A Real-World Gap in Survivorship Care","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePopulation aging has led to a substantial rise in cancer incidence worldwide, with older adults accounting for most new diagnoses and cancer-related mortality. Advances in early detection and oncologic therapies have significantly improved survival, resulting in a rapidly growing population of cancer survivors. As malignancy increasingly becomes a chronic condition, survivorship care must extend beyond tumor control to address long-term treatment-related complications, including skeletal health (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSkeletal morbidity represents a major but often under-recognized component of cancer survivorship. Osteoporosis and fragility fractures are common age-related conditions, and their risk is further amplified in patients with cancer due to both the malignant process itself and cancer-directed therapies (\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Cancer-treatment induced bone loss (CTIBL) is a multifactorial phenomenon driven by systemic inflammation, reduced physical activity, nutritional deficiencies, and exposure to bone-depleting treatments such as chemotherapy, radiotherapy, endocrine therapies, glucocorticoids, and hematopoietic stem-cell transplantation (\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). These mechanisms may act synergistically to accelerate bone turnover, reduce bone mineral density (BMD), and increase fracture risk. Bone health complications are well established in hormone-sensitive malignancies, particularly breast and prostate cancer, where aromatase inhibitors and androgen deprivation therapy are strongly associated with accelerated bone loss and increased fracture risk (\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Consequently, clinical awareness and guideline recommendations regarding bone health assessment are more clearly defined in these populations. However, accumulating evidence indicates that skeletal fragility is not confined to hormone-sensitive cancers. Population-based studies have demonstrated an increased fracture risk across a broad range of malignancies, with risk persisting for years after cancer diagnosis, underscoring the broader relevance of bone health in oncology (\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite this elevated risk, osteoporosis risk assessment tools frequently underestimate fracture risk in cancer survivors, and a history of malignancy is not consistently incorporated into fracture prediction models (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). International clinical practice guidelines from oncology and endocrine societies, including the European Society for Medical Oncology, the American Society of Clinical Oncology, and the Endocrine Society, recommend bone health evaluation using dual-energy X-ray absorptiometry (DXA) in patients at increased skeletal risk, particularly those exposed to bone-toxic therapies (\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Nevertheless, real-world studies consistently demonstrate low rates of DXA utilization and osteoporosis management among oncology patients, even within well-defined high-risk groups (\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eData describing bone health assessment patterns across unselected cancer populations in routine care remain limited. In particular, there is a paucity of large real-world analyses evaluating the timing and uptake of DXA assessment following cancer diagnosis, as well as the extent to which bone loss is already present at the time of evaluation. Understanding these patterns is essential for identifying gaps in care and informing strategies for more integrated bone\u0026ndash;oncology and survivorship pathways.\u003c/p\u003e \u003cp\u003eThe present study aimed to evaluate DXA utilization following a new cancer diagnosis within a large real-world oncology cohort treated in a private healthcare network in Israel. Specifically, we examined the frequency and timing of DXA assessment, BMD outcomes, and patient characteristics associated with DXA referral, in order to identify disparities in bone health evaluation and highlight opportunities for earlier skeletal risk identification.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Data Source\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study using the MDClone platform, which allows generation of fully anonymized yet statistically accurate datasets. Adults (\u0026ge;\u0026thinsp;18 years) with a first documented cancer diagnosis in Assuta Medical Centers between January 1, 2015, and December 31, 2024 were included. Assuta Medical Centers is a leading private healthcare provider in Israel, operating four hospitals and multiple diagnostic centers across the country. The network provides a broad range of medical services, including oncology, surgery, and advanced imaging, and serves over two million patients annually. Assuta is owned by Maccabi Healthcare Services, one of Israel\u0026rsquo;s largest health maintenance organizations, and treats patients through both insured and private frameworks. The study cohort therefore represents real-world care patterns within a large private healthcare network with broad national reach.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Population and Inclusion Criteria\u003c/h2\u003e \u003cp\u003ePatients were eligible if they were aged 18 years or older, had a new diagnosis of any malignancy during the study period, and had complete demographic data. Patients with missing essential demographic information were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Collected\u003c/h2\u003e \u003cp\u003eFor all subjects with a newly diagnosed malignancy treated in Assuta Medical Centers, we collected demographics age, sex, BMI, clinical characteristics: smoking status, cancer type, and the treatment received. Cancer diagnosis and site were defined according to the recorded organ of origin at the time of diagnosis, based on structured electronic medical record documentation. Data on oncologic treatments were extracted from structured electronic medical records using the MDClone platform and included documented exposure to chemotherapy, endocrine therapy, biologic agents, radiotherapy, and surgical procedures. Bone mineral density was assessed by dual-energy X-ray absorptiometry (DXA), performed using GE Lunar DXA (GE Healthcare, Madison, WI, USA) systems. We extracted DXA parameters: lumbar spine, total hip, and femoral neck BMD and T-scores. We calculated the time from cancer diagnosis to DXA completion, and defined bone status categories: osteoporosis (T-score \u0026le; \u0026minus;\u0026thinsp;2.5) and osteopenia (T-score between \u0026minus;\u0026thinsp;1.0 and \u0026minus;\u0026thinsp;2.5 according to WHO criteria using the lowest T-score obtained from lumbar spine, femoral neck, or total hip measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Outcomes\u003c/h2\u003e \u003cp\u003ePrimary outcome: Proportion of cancer patients undergoing DXA.\u003c/p\u003e \u003cp\u003eSecondary outcomes were the time between the oncologic diagnosis and DXA scan performance, BMD/T-score distribution, prevalence of osteoporosis and osteopenia, and the distribution of DXA testing by cancer type.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables as counts and percentages. Comparisons between patients who underwent DXA assessment and those who did not were performed using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test for continuous variables, as appropriate, and χ\u0026sup2; test for categorical variables. Time from cancer diagnosis to DXA assessment was evaluated descriptively, and cumulative incidence curves were used to illustrate differences in timing according to sex. Multivariable logistic regression analysis was performed to identify factors independently associated with DXA assessment following cancer diagnosis. Covariates included age, sex, cancer type (grouped by hormone sensitivity), and documented exposure to chemotherapy and radiotherapy.\u003c/p\u003e \u003cp\u003eResults are presented as adjusted odds ratios with 95% confidence intervals. All analyses were descriptive and exploratory in nature, aimed at characterizing patterns of bone health assessment and identifying disparities in care within the oncology population. Statistical analyses were conducted using IBM SPSS Statistics software (version 29), and a two-sided \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study Population\u003c/h2\u003e \u003cp\u003eThe study included 21,112 adults with a first diagnosis of malignancy between 01.01.2015 and 31.12.2024. Women comprised 77.0% of the cohort (n\u0026thinsp;=\u0026thinsp;16,244), while 22.9% were men (n\u0026thinsp;=\u0026thinsp;4,838). The mean age of the cohort was 60.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1 years, and mean BMI was 26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4 kg/m\u0026sup2;. Documented current or former smoking was present in 11,059 patients (52.4%). Documented chemotherapy exposure was recorded in 4,512 patients (21.4%) and radiotherapy in 1,473 (7.0%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.).\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\u003eBaseline Characteristics of the Study Cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal cohort (n\u0026thinsp;=\u0026thinsp;21,112)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,244 (77.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (BMI), kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent or former smoking\u0026dagger;, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,059 (52.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,851 (41.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate cancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,411 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther malignancies, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,850 (51.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDXA performed after cancer diagnosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,496 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDXA within 6 months of diagnosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,550 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocumented chemotherapy exposure\u0026dagger;, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,512 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocumented radiotherapy exposure\u0026dagger;, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,473 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026dagger; As documented in the medical record.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Utilization of Bone Density Assessment\u003c/h2\u003e \u003cp\u003eOverall, 7,496 patients (35.5%) underwent DXA assessment following cancer diagnosis, of whom 1,550 (7.3%) were evaluated within 6 months of diagnosis. Thus, nearly two-thirds of oncology patients did not receive objective skeletal evaluation, despite known cancer- and treatment-related bone risks. Patients who underwent DXA differed significantly from those who did not, being older, more frequently female, and more likely to have hormone-sensitive malignancies. Documented smoking status and oncologic treatment exposures were substantially more frequent among DXA performers (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) possibly reflecting more comprehensive clinical documentation rather than true differences in exposure prevalence. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eCharacteristics of Patients With and Without DXA Assessment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDXA performed (n\u0026thinsp;=\u0026thinsp;7,496)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo DXA performed\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;13,616)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,771 (90.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,473 (69.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent or former smoking\u0026dagger;, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,372 (85.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,687 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocumented chemotherapy exposure\u0026dagger;, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,512 (60.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,906 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocumented radiotherapy exposure\u0026dagger;, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,473 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e681 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,782 (49.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,067 (37.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate cancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e440 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e971 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ep values derived from χ\u0026sup2; tests for categorical variables and Student\u0026rsquo;s t-test or Mann\u0026ndash;\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eWhitney U test for continuous variables, as appropriate.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026dagger; Smoking status and oncologic treatment exposure were incompletely captured in the\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eelectronic health record and are reported as documented exposure only.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sex Disparities in DXA Assessment\u003c/h2\u003e \u003cp\u003eDXA utilization differed markedly by sex. Women were significantly more likely to undergo DXA than men (41.7% vs. 15.0%). Male sex was associated with a substantially lower likelihood of DXA assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. DXA Assessment by Cancer Type and treatment\u003c/h2\u003e \u003cp\u003eDXA utilization varied significantly by cancer type. The highest rates were observed in breast cancer (42.2%) and prostate cancer (30.0%), accounting for approximately half of all DXA examinations. In contrast, DXA assessment was infrequent in lung, gastrointestinal, ovarian, and pancreatic cancers, with utilization rates generally below 15%. Using an organ-based classification, hematologic malignancies accounted for approximately 6\u0026ndash;8% of the overall cohort and 4\u0026ndash;5% of patients who underwent DXA assessment (Fig.\u0026nbsp;1.). Among patients who underwent DXA, 4,512 (\u0026asymp;\u0026thinsp;60%) had documented exposure to chemotherapy and 1,473 (\u0026asymp;\u0026thinsp;20%) had received radiotherapy.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFigure 1. Proportion of patients undergoing DXA assessment following cancer diagnosis, stratified by cancer type.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e*\u003cem\u003eHematologic malignancies were identified using an organ-based classification and may be under-ascertained.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Timing of DXA Assessment\u003c/h2\u003e \u003cp\u003eEarly skeletal evaluation was uncommon. Only 1,550 patients (7.3%) underwent DXA within 6 months of cancer diagnosis. Among those who underwent DXA, the median time to assessment was 14.8 months, indicating that bone health evaluation was generally delayed and not integrated into early oncologic care. Early DXA assessment within 6 months of diagnosis was uncommon (1,550 patients; 7.3% of the total cohort), and only 14.1% underwent DXA within the first 12 months.\u003c/p\u003e \u003cp\u003eAlthough women were significantly more likely than men to undergo DXA, timing among those evaluated was broadly comparable between sexes (Fig.\u0026nbsp;2A). Median time from cancer diagnosis to DXA differed by malignancy type. Among breast cancer patients (n\u0026thinsp;=\u0026thinsp;3,516), median time to DXA was 12.9 months (IQR 5.8\u0026ndash;28.0). In prostate cancer (n\u0026thinsp;=\u0026thinsp;390), median time was 12.7 months (IQR 4.4\u0026ndash;28.8). In contrast, patients with other malignancies (n\u0026thinsp;=\u0026thinsp;3,019) experienced a substantially longer delay, with a median time of 17.9 months (IQR 8.3\u0026ndash;35.1) (Fig.\u0026nbsp;2B). Patients without DXA were administratively censored at the end of the study period.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFigure 2A. Cumulative distribution of time to DXA assessment after cancer diagnosis, stratified by sex.\u003c/span\u003e Curves represent the cumulative proportion of patients undergoing DXA over time among those who received bone density assessment. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2B. Cumulative distribution of time to DXA assessment after cancer diagnosis stratified by cancer type.\u003c/span\u003e Curves represent the cumulative proportion of patients undergoing DXA over time among DXA performers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Bone Density Findings\u003c/h2\u003e \u003cp\u003eAmong patients who underwent DXA assessment (n\u0026thinsp;=\u0026thinsp;5,570 with complete evaluable measurements), bone mineral density at the time of evaluation was frequently already abnormal. Using the lowest T-score across lumbar spine, femoral neck, and total hip, 2,520 patients (45.2%) met criteria for osteopenia and 809 (14.5%) met criteria for osteoporosis, whereas only 2,241 (40.2%) had normal BMD. Among patients with osteopenia, the mean lowest T-score was \u0026minus;\u0026thinsp;1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39, suggesting established low bone mass rather than marginal diagnostic values. In those meeting criteria for osteoporosis, the mean lowest T-score was \u0026minus;\u0026thinsp;2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47, indicating clinically significant skeletal compromise well below the diagnostic threshold (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At cortical skeletal sites, bone loss was particularly evident. Among patients classified as osteoporotic, femoral neck values frequently approached or exceeded the threshold associated with increased hip fracture risk. These findings suggest that DXA assessment was often performed after substantial bone loss had already occurred.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBone Mineral Density T-Score Results among DXA Performers\u003c/span\u003e (n\u0026thinsp;=\u0026thinsp;5,570*)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnostic category (lowest T-score)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean lowest T-score\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (T\u0026thinsp;\u0026ge;\u0026thinsp;\u0026minus;\u0026thinsp;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteopenia (\u0026minus;\u0026thinsp;1.0\u0026thinsp;\u0026gt;\u0026thinsp;T\u0026thinsp;\u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoporosis (T\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*Includes patients with complete evaluable DXA measurements across skeletal sites.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn a multivariable logistic regression analysis, female sex was independently associated with higher odds of undergoing DXA assessment compared with male sex (adjusted OR 2.13, 95% CI 1.79\u0026ndash;2.50; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Increasing age was also associated with greater odds of DXA assessment (aOR per year 1.03, 95% CI 1.03\u0026ndash;1.03; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with breast cancer and prostate cancer had markedly higher odds of DXA assessment compared with patients with other cancer types (aOR 5.82 and 5.29, respectively; both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Collinearity between covariates was assessed and no clinically meaningful multi-collinearity was identified (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMultivariable Logistic Regression Analysis of Factors Associated With DXA\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePerformance\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR (aOR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex (vs male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.79\u0026ndash;2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer (vs other cancers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.15\u0026ndash;6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate cancer (vs other cancers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.42\u0026ndash;6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this large real-world oncology cohort, bone mineral density assessment following a new cancer diagnosis was markedly underutilized and frequently delayed. Fewer than 40% of patients underwent DXA at any time after diagnosis, and only a small minority were evaluated within the first 6 months. Despite low screening rates, nearly two-thirds of patients who underwent DXA had abnormal bone density, and more than one in six met criteria for osteoporosis. These findings highlight a substantial gap between guideline recommendations and routine clinical practice, underscoring the need for improved integration of bone health assessment into oncology care pathways.\u003c/p\u003e \u003cp\u003eImportantly, the DXA-performing cohort was heavily enriched with breast cancer patients, who accounted for approximately 42% of all bone density assessments. Given that breast cancer represents a population with well-established guideline recommendations for skeletal monitoring, one might expect high rates of early DXA integration. Nevertheless, overall DXA utilization in breast cancer remained below 50%, and early assessment within six months of diagnosis was uncommon. These findings suggest that gaps in bone health evaluation persist even within guideline-targeted populations and are not confined to non\u0026ndash;hormone-sensitive malignancies. Although breast cancer comprised the largest single cancer type, more than half of the cohort (51.4%) consisted of non\u0026ndash;breast and non\u0026ndash;prostate malignancies, providing a broad representation of solid and hematologic cancers in routine practice. In this group, DXA use was substantially lower, despite well-established skeletal risks related to systemic therapy, corticosteroids, and transplantation. This gap is particularly relevant in the modern therapeutic era. The skeletal effects of newer systemic therapies\u0026mdash;including immune checkpoint inhibitors and targeted agents\u0026mdash;remain incompletely characterized. Emerging mechanistic and observational data suggest that immune modulation may influence bone remodeling pathways; however, longitudinal bone mineral density and fracture outcomes remain insufficiently defined (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In a recent meta-analysis of randomized trials involving over 19,000 patients, immune checkpoint inhibitor use was associated with a non-significant 18% increase in clinical fracture incidence compared with controls (OR 1.18, 95% CI 0.82\u0026ndash;1.70), although fracture events were infrequently reported and estimates were imprecise (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Together, these findings highlight ongoing uncertainty regarding long-term skeletal risk in the era of immune modulation. In this evolving therapeutic landscape, reliance on traditional risk frameworks may underestimate bone vulnerability across contemporary cancer care.\u003c/p\u003e \u003cp\u003eSex-based disparities were also evident. Women were more likely to undergo DXA assessment. While this may partly reflect the predominance of breast cancer and greater familiarity with osteoporosis screening in women, it raises concern that men with cancer\u0026mdash;particularly those receiving bone-depleting therapies\u0026mdash;remain underserved with respect to bone health evaluation.\u003c/p\u003e \u003cp\u003eThe delayed timing of DXA observed in this cohort further emphasizes missed opportunities for early intervention. Bone loss associated with cancer therapies often occurs early in the treatment course, and timely identification of low bone mass allows for preventive strategies that may reduce fracture risk and preserve functional independence.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Its retrospective design and reliance on electronic health record data introduce the potential for incomplete or inconsistent documentation, particularly for treatment exposures and lifestyle factors. Information on cancer histology, disease stage, and treatment dose or duration\u0026mdash;factors that may influence skeletal outcomes\u0026mdash;was not uniformly available and could not be fully incorporated into the analysis. Hematologic malignancies and stem-cell transplantation could not be systematically identified, precluding detailed subgroup analyses in these high-risk populations. DXA examinations performed outside Assuta Medical Centers could not be captured in the present dataset. However, given that bone density testing is routinely reimbursed and widely available within the network, performance of DXA at external facilities is considered unlikely, and any resulting under-ascertainment is expected to be limited. Additionally, DXA performance was used as a surrogate for bone health evaluation, and fracture outcomes were not assessed. Nonetheless, the large cohort size and real-world setting provide valuable insight into current practice patterns and highlight clinically relevant gaps in care. Importantly, more than half of the cohort consisted of non\u0026ndash;breast and non\u0026ndash;prostate malignancies, ensuring broad representation beyond traditionally studied hormone-sensitive cancers.\u003c/p\u003e \u003cp\u003eIn summary, our real-world experience reveals that bone density assessment following cancer diagnosis is often delayed and selectively applied, despite a high prevalence of low bone mass at the time of evaluation. These observations highlight missed opportunities for early skeletal risk identification and support more proactive incorporation of bone health assessment into routine oncology and survivorship care.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn conclusion, in our real-world experience, bone density assessment following cancer diagnosis was infrequently performed and commonly delayed, with substantial disparities according to sex and cancer type. These observations should be interpreted in the context of current guideline recommendations, which primarily target patients in hormone-sensitive cancers receiving endocrine therapies or those with additional fracture risk factors. Nevertheless, the high prevalence of low bone mass among patients who underwent DXA suggests that skeletal risk is often recognized only after clinically relevant bone loss has already occurred. Collectively, these findings support the need for more systematic and proactive integration of bone health evaluation into oncology and survivorship care, including populations beyond traditionally recognized high-risk malignancies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed by the Assuta Medical Centers Institutional Review Board and was granted exemption from formal ethical approval due to the use of fully anonymized retrospective data. Informed consent was waived in accordance with institutional and national guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author (Vanessa Rouach) used ChatGPT\u003c/p\u003e\n\u003cp\u003e(OpenAI) to assist with language editing and refinement of the text. After using this\u003c/p\u003e\n\u003cp\u003etool, the author reviewed and edited the content as needed and takes full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBrennan M, Kalsi T. Management of bone health considerations in patients with cancer. Cancers (Basel). 2025;17:2878. doi:10.3390/cancers17172878.\u003c/li\u003e\n \u003cli\u003eLink-Rachner CS, G\u0026ouml;bel A, Jaschke NP, Rachner TD. Endocrine health in survivors of adult-onset cancer. Lancet Diabetes Endocrinol. 2024;12:350\u0026ndash;364. doi:10.1016/S2213-8587(24)00088-3\u003c/li\u003e\n \u003cli\u003eReid DM, Doughty J, Eastell R, Heys SD, Howell A, McCloskey EV, Powles T, Selby P, Coleman RE. Guidance for the management of breast cancer treatment-induced bone loss: a consensus position statement from a UK Expert Group. Cancer Treat Rev. 2008;34 Suppl 1:S3-18. doi: 10.1016/j.ctrv.2008.03.007. Epub 2008 Jun 2. PMID: 18515009.\u003c/li\u003e\n \u003cli\u003eAapro M, Abrahamsson PA, Body JJ, Coleman RE, Colomer R, Costa L, Crin\u0026ograve; L, Dirix L, Gnant M, Gralow J, Hadji P, Hortobagyi GN, Jonat W, Lipton A, Monnier A, Paterson AH, Rizzoli R, Saad F, Th\u0026uuml;rlimann B. Guidance on the use of bisphosphonates in solid tumours: recommendations of an international expert panel. Ann Oncol. 2008 Mar;19(3):420-32. doi: 10.1093/annonc/mdm442. Epub 2007 Sep 28. PMID: 17906299.\u003c/li\u003e\n \u003cli\u003eWaqas K, Lima Ferreira J, Tsourdi E, Body JJ, Hadji P, Zillikens MC. Updated guidance on the management of cancer treatment-induced bone loss (CTIBL) in pre- and postmenopausal women with early-stage breast cancer. J Bone Oncol. 2021 Mar 18;28:100355. doi: 10.1016/j.jbo.2021.100355. PMID: 33948427; PMCID: PMC8080519.\u003c/li\u003e\n \u003cli\u003eRayner-Myers SD, Hunter K, Pituskin E. Direct and Indirect Mechanisms of Chemotherapy-Induced Bone Loss in Adjuvant Breast Cancer: An Integrative Review. Semin Oncol Nurs. 2022 Apr;38(2):151280. doi: 10.1016/j.soncn.2022.151280. Epub 2022 Apr 25. PMID: 35477650.\u003c/li\u003e\n \u003cli\u003ePacheco R, Stock H. Effects of radiation on bone. Curr Osteoporos Rep. 2013 Dec;11(4):299-304. doi: 10.1007/s11914-013-0174-z. PMID: 24057133.\u003c/li\u003e\n \u003cli\u003eDonaubauer AJ, Deloch L, Becker I, Fietkau R, Frey B, Gaipl US. The Influence of Radiation on Bone and Bone Cells-Differential Effects on Osteoclasts and Osteoblasts. Int J Mol Sci. 2020 Sep 2;21(17):6377. doi: 10.3390/ijms21176377. PMID: 32887421; PMCID: PMC7504528.\u003c/li\u003e\n \u003cli\u003eAxelsen CT, Jensen AB, Jakobsen EH, Bechmann T. Bone loss during neoadjuvant/adjuvant chemotherapy for early-stage breast cancer. Mol Clin Oncol. 2018;8:767\u0026ndash;772. doi:10.3892/mco.2018.1615.\u003c/li\u003e\n \u003cli\u003eBouvard B, Soulat G, H\u0026eacute;lis V, et al. Bone loss and fracture risk after chemotherapy for breast cancer. Osteoporos Int. 2021;32:1683\u0026ndash;1691. doi:10.1007/s00198-021-05914-z.\u003c/li\u003e\n \u003cli\u003eHadji P, Aapro M, Body JJ, et al. Management of aromatase inhibitor\u0026ndash;associated bone loss. Lancet Oncol. 2017;18:e319\u0026ndash;e328. doi:10.1016/S1470-2045(17)30036-5.\u003c/li\u003e\n \u003cli\u003eSaad F, Adachi JD, Brown JP, Canning LA, Gelmon KA, Josse RG, Pritchard KI. Cancer treatment-induced bone loss in breast and prostate cancer. J Clin Oncol. 2008 Nov 20;26(33):5465-76. doi: 10.1200/JCO.2008.18.4184. Epub 2008 Oct 27. PMID: 18955443.\u003c/li\u003e\n \u003cli\u003eHadji P, Aapro MS, Body JJ, Bundred NJ, Brufsky A, Coleman RE, Gnant M, Guise T, Lipton A. Management of aromatase inhibitor-associated bone loss in postmenopausal women with breast cancer: practical guidance for prevention and treatment. Ann Oncol. 2011 Dec;22(12):2546-2555. doi: 10.1093/annonc/mdr017. Epub 2011 Mar 17. PMID: 21415233.\u003c/li\u003e\n \u003cli\u003eKhan MI. Management of bone loss due to endocrine therapy during cancer treatment. Osteoporos Int. 2023;34:671\u0026ndash;680. doi:10.1007/s00198-023-06672-3.\u003c/li\u003e\n \u003cli\u003eTakahashi S. Management of cancer treatment\u0026ndash;induced bone loss in breast and prostate cancer. J Bone Miner Metab. 2023;41:307\u0026ndash;316. doi:10.1007/s00774-023-01414-1.\u003c/li\u003e\n \u003cli\u003eLiang I, Brennan S, Girgis C, Hayden A, Moujaber T, Turner S, Vasista A, Grossmann M, Wong PKK. Bone Health Management in Men Commencing Androgen Deprivation Therapy for Prostate Cancer and Women Commencing Anti-Oestrogen Therapy for Breast Cancer. Cancer Med. 2025 May;14(9):e70873. doi: 10.1002/cam4.70873. PMID: 40357899; PMCID: PMC12070255.\u003c/li\u003e\n \u003cli\u003eBrown JE, Handforth C, Compston JE, Cross W, Parr N, Selby P, Wood S, Drudge-Coates L, Walsh JS, Mitchell C, Collinson FJ, Coleman RE, James N, Francis R, Reid DM, McCloskey E. Guidance for the assessment and management of prostate cancer treatment-induced bone loss. A consensus position statement from an expert group. J Bone Oncol. 2020 Aug 2;25:100311. doi: 10.1016/j.jbo.2020.100311. PMID: 32995252; PMCID: PMC7516275.\u003c/li\u003e\n \u003cli\u003eJones C, Gray S, Brown M, Brown J, McCloskey E, Rai BP, Clarke N, Sachdeva A. Risk of Fractures and Falls in Men with Advanced or Metastatic Prostate Cancer Receiving Androgen Deprivation Therapy and Treated with Novel Androgen Receptor Signalling Inhibitors: A Systematic Review and Meta-analysis of Randomised Controlled Trials. Eur Urol Oncol. 2024 Oct;7(5):993-1004. doi: 10.1016/j.euo.2024.01.016. Epub 2024 Feb 20. PMID: 38383277.\u003c/li\u003e\n \u003cli\u003eTeissonni\u0026egrave;re M, Point M, Biver E, Hadji P, Bonnelye E, Ebeling PR, Kendler D, de Villiers T, Holzer G, Body JJ, Fuleihan GEH, Brandi ML, Rizzoli R, Confavreux CB. Bone Effects of Anti-Cancer Treatments in 2024. Calcif Tissue Int. 2025 Mar 27;116(1):54. doi: 10.1007/s00223-025-01362-0. PMID: 40146323; PMCID: PMC11950069.\u003c/li\u003e\n \u003cli\u003eRizzoli R, Body JJ, Brandi ML, Cannata-Andia J, Chappard D, El Maghraoui A, Gl\u0026uuml;er CC, Kendler D, Napoli N, Papaioannou A, Pierroz DD, Rahme M, Van Poznak CH, de Villiers TJ, El Hajj Fuleihan G; International Osteoporosis Foundation Committee of Scientific Advisors Working Group on Cancer-Induced Bone Disease. Cancer-associated bone disease. Osteoporos Int. 2013 Dec;24(12):2929-53. doi: 10.1007/s00198-013-2530-3. Epub 2013 Oct 22. PMID: 24146095; PMCID: PMC5104551.\u003c/li\u003e\n \u003cli\u003eYe C, Leslie WD. Fracture risk and assessment in adults with cancer. Osteoporos Int. 2023 Mar;34(3):449-466. doi: 10.1007/s00198-022-06631-4. Epub 2022 Dec 13. PMID: 36512057.\u003c/li\u003e\n \u003cli\u003eLeslie WD, Edwards B, Al-Azazi S, Yan L, Lix LM, Czaykowski P, Singh H. Cancer patients with fractures are rarely assessed or treated for osteoporosis: a population-based study. Osteoporos Int. 2021 Feb;32(2):333-341. doi: 10.1007/s00198-020-05596-6. Epub 2020 Aug 17. PMID: 32808139\u003c/li\u003e\n \u003cli\u003eColeman R, Hadji P, Body JJ, et al. Bone health in cancer: ESMO Clinical Practice Guidelines. Ann Oncol. 2020;31:1650\u0026ndash;1663. doi:10.1016/j.annonc.2020.07.019.\u003c/li\u003e\n \u003cli\u003eShapiro CL, Van Poznak C, Lacchetti C, et al. Management of osteoporosis in survivors of adult cancers. J Clin Oncol. 2019;37:2916\u0026ndash;2946. doi:10.1200/JCO.19.01696.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e25.\u003c/strong\u003e Rizzoli R, Body JJ, Brandi ML, Cannata-Andia J, Chappard D, El Maghraoui A, Gl\u0026uuml;er CC, Kendler D, Napoli N, Papaioannou A, Pierroz DD, Rahme M, Van Poznak CH, de Villiers TJ, El Hajj Fuleihan G; International Osteoporosis Foundation Committee of Scientific Advisors Working Group on Cancer-Induced Bone Disease. Cancer-associated bone disease. Osteoporos Int. 2013 Dec;24(12):2929-53. doi: 10.1007/s00198-013-2530-3. Epub 2013 Oct 22. PMID: 24146095; PMCID: PMC5104551.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e26.\u003c/strong\u003e Eastell R, Rosen CJ, Black DM, Cheung AM, Murad MH, Shoback D. Pharmacological management of osteoporosis in postmenopausal women: an Endocrine Society clinical practice guideline. \u003cem\u003eJ Clin Endocrinol Metab.\u003c/em\u003e 2019;104(5):1595\u0026ndash;1622. doi:10.1210/jc.2019-00221\u003c/li\u003e\n \u003cli\u003eTseng OL, Dawes MG, Spinelli JJ, Gotay CC, McBride ML. Utilization of bone mineral density testing among breast cancer survivors in British Columbia, Canada. Osteoporos Int. 2017 Dec;28(12):3439-3449. doi: 10.1007/s00198-017-4218-6. Epub 2017 Oct 9. PMID: 28993862.\u003c/li\u003e\n \u003cli\u003eStratton J, Hu X, Soulos PR, Davidoff AJ, Pusztai L, Gross CP, Mougalian SS. Bone Density Screening in Postmenopausal Women With Early-Stage Breast Cancer Treated With Aromatase Inhibitors. J Oncol Pract. 2017 May;13(5):e505-e515. doi: 10.1200/JOP.2016.018341. Epub 2017 Mar 7. PMID: 28267392.\u003c/li\u003e\n \u003cli\u003eBailey S, Lin J. The association of osteoporosis knowledge and beliefs with preventive behaviors in postmenopausal breast cancer survivors. BMC Womens Health. 2021 Aug 11;21(1):297. doi: 10.1186/s12905-021-01430-1. PMID: 34380488; PMCID: PMC8359538.\u003c/li\u003e\n \u003cli\u003eRouach V, Greenman Y, Chodick G, Goldshtein I. DXA assessment and fracture prevention after aromatase inhibitor initiation. J Bone Oncol. 2023;42:100501. doi:10.1016/j.jbo.2023.100501.\u003c/li\u003e\n \u003cli\u003eHarmouch SS, Berger AJ, Cole AP. Low rates of bone density testing in prostate cancer survivors on androgen-deprivation therapy: where do we go from here? BJU Int. 2018 Apr;121(4):492-493. doi: 10.1111/bju.14120. PMID: 29603897.\u003c/li\u003e\n \u003cli\u003eWang JW, Dai MW, Liu JH. The regulatory effects of PD-1/PD-L1 inhibitors on bone metabolism: opportunities and challenges in osteoporosis management. Front Immunol. 2025 Jul 25;16:1630751. doi: 10.3389/fimmu.2025.1630751. PMID: 40787445; PMCID: PMC12331705.\u003c/li\u003e\n \u003cli\u003eSiampanopoulou V, Ziogas DC, Lyrarakis G, Anastasopoulou A, Kassi E, Gogas H, Angelousi A. Bone mineral density changes following immune checkpoint inhibitor therapy: insights from a case series analysis. Osteoporos Int. 2025 Sep;36(9):1711-1718. doi: 10.1007/s00198-025-07647-2. Epub 2025 Aug 10. PMID: 40783905.\u003c/li\u003e\n \u003cli\u003eYavropoulou MP, Anastasilakis DA, Kasdagli MI, Gialouri CG, Palaiopanos K, Fountas A, Anastasilakis AD, Daskalakis K, Dekkers OM, Lems WF, Papapoulos SE, Makras P. Incidence of fractures in patients with solid cancers treated with immune checkpoint inhibitors: a systematic review and meta-analysis of randomised controlled trials. BMJ Oncol. 2025 Nov 24;4(1):e000868. doi: 10.1136/bmjonc-2025-000868. PMID: 41323423; PMCID: PMC12658493.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"cancer treatment-induced bone loss (CTIBL), Dual X-Ray Absorptiometry (DXA), osteoporosis, osteopenia, bone mineral density, fracture prevention","lastPublishedDoi":"10.21203/rs.3.rs-8955916/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8955916/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Cancer treatment–induced bone loss (CTIBL) is well recognized in hormone-sensitive malignancies such as breast and prostate cancer but is less consistently implemented in patients with other solid tumors and hematologic malignancies. Furthermore, the long-term skeletal effects of newer systemic therapies, including immune checkpoint inhibitors and targeted agents, remain incompletely characterized. Although bone mineral density (BMD) assessment by dual-energy X-ray absorptiometry (DXA) is recommended for patients at increased skeletal risk, real-world utilization patterns remain poorly characterized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives: \u003c/strong\u003eTo evaluate DXA utilization, timing, BMD results, and associated clinical characteristics following a new cancer diagnosis in a large real-world cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a retrospective cohort study using the MDClone platform, including adults with a first cancer diagnosis between January 1, 2015, and December 31, 2024, treated at Assuta Medical Centers in Israel. DXA completion, timing, BMD and T-scores, and prevalence of osteopenia and osteoporosis were analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eResults: Among 21,112 patients with newly diagnosed cancer (77.0% women; mean age 60.2 ± 13.1 years), 7,496 (35.5%) underwent DXA at any time following diagnosis. DXA utilization was significantly lower in men than in women (15.0% vs. 41.7%). Early DXA assessment within 6 months of diagnosis was uncommon (1,550 patients; 7.3% of the total cohort), and only 14.1% underwent DXA within the first 12 months. Median time from cancer diagnosis to DXA was 14.8 months. DXA utilization varied by cancer type and was highest in breast cancer (39.7%), compared with prostate cancer (27.6%) and other malignancies (27.8%). Among patients undergoing DXA, 40.2% had normal BMD, 45.2% had osteopenia, and 14.5% met criteria for osteoporosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: In our real-world experience within a large private healthcare network, DXA assessment after cancer diagnosis is infrequently performed and often delayed, with marked disparities by sex and cancer type. A substantial burden of low bone mass is already present at the time of evaluation, highlighting missed opportunities for early skeletal risk identification.\u003c/p\u003e","manuscriptTitle":"Delayed and Selective Bone Health Assessment Following Cancer Diagnosis: A Real-World Gap in Survivorship Care","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 09:15:05","doi":"10.21203/rs.3.rs-8955916/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-31T19:22:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T19:21:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-26T00:09:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Supportive Care in Cancer","date":"2026-02-24T10:01:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a9baefa3-b3ba-4f04-afae-0c13f4a60bbb","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T09:15:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 09:15:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8955916","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8955916","identity":"rs-8955916","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}