Development of a new clinical nomogram to predict bone metastasis in luminal A breast cancer patients: A SEER-based study

Development of a new clinical nomogram to predict bone metastasis in luminal A breast cancer patients: A SEER-based study | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 March 2025 V1 Latest version Share on Development of a new clinical nomogram to predict bone metastasis in luminal A breast cancer patients: A SEER-based study Authors : Zehao Cai , Qiuyan Luo , Yong Fu , Han Li , shengchun liu 0000-0002-9933-3643 , and Yang Peng 0000-0002-4146-8369 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174186569.92563323/v1 123 views 73 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: Luminal A breast cancer (LABC) is the most common subtype with bone metastasis. Identifying high-risk patients for bone metastasis early is essential for improving outcomes. This research focused on creating and validating a nomogram to assess the risk of BM in patients with LABC. Methods: We extracted data for 236,132 LABC patients from the SEER database covering the years 2010 to 2015. Patients diagnosed between 2010 and 2013 composed the training set (n=152,850), whereas those diagnosed between 2014 and 2015 composed the validation set (n=83,282). Logistic regression analyses identified predictive factors for BM. A nomogram was developed and validated through ROC curve analysis and calibration plots. A total of 2.1% of the training cohort and 2.2% of the validation cohort developed BM. T stage, N stage, and marital status were significant predictors of BM risk. The nomogram exhibited strong discriminative performance, with an AUC of 0.894 (95% CI: 0.890-0.899) in the training set and 0.846 (95% CI: 0.837-0.856) in the validation set. The calibration plots demonstrated strong concordance between the predicted and observed BM rates across both cohorts. Conclusion: This study established a clinically relevant nomogram for predicting BM risk in LABC patients. The model’s strong predictive ability suggest its potential as a valuable tool for risk stratification and personalized patient management. Further external validation is warranted to confirm its generalizability across diverse populations. This study focused on developing a risk prediction model for bone metastasis in LABC patients using a nomogram based on data from the SEER database Development of a new clinical nomogram to predict bone metastasis in luminal A breast cancer patients: A SEER-based study Zehao Cai 1# , Qiuyan Luo 1# , Yong Fu 2 , Han Li 1 , Shengchun Liu 1 *, Yang Peng 1 * 1 Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China 400016 2 Department of Breast Surgery, Dianjiang People’s Hospital of Chongqing, Chongqing, China 408300 Yang Peng’s email adress : [email protected] Background: Luminal A breast cancer (LABC) is the most common subtype with bone metastasis. Identifying high-risk patients for bone metastasis early is essential for improving outcomes. This research focused on creating and validating a nomogram to assess the risk of BM in patients with LABC. Methods: We extracted data for 236,132 LABC patients from the SEER database covering the years 2010 to 2015. Patients diagnosed between 2010 and 2013 composed the training set (n=152,850), whereas those diagnosed between 2014 and 2015 composed the validation set (n=83,282). Logistic regression analyses identified predictive factors for BM. A nomogram was developed and validated through ROC curve analysis and calibration plots. A total of 2.1% of the training cohort and 2.2% of the validation cohort developed BM. T stage, N stage, and marital status were significant predictors of BM risk. The nomogram exhibited strong discriminative performance, with an AUC of 0.894 (95% CI: 0.890-0.899) in the training set and 0.846 (95% CI: 0.837-0.856) in the validation set. The calibration plots demonstrated strong concordance between the predicted and observed BM rates across both cohorts. Conclusion: This study established a clinically relevant nomogram for predicting BM risk in LABC patients. The model’s strong predictive ability suggest its potential as a valuable tool for risk stratification and personalized patient management. Further external validation is warranted to confirm its generalizability across diverse populations. This study focused on developing a risk prediction model for bone metastasis in LABC patients using a nomogram based on data from the SEER database. Synopsis: This study developed a novel nomogram to predict bone metastasis (BM) risk in luminal A breast cancer (LABC) patients using SEER database data (2010–2015). The model, incorporating T stage, N stage, and marital status, demonstrated strong predictive accuracy (AUC 0.894 in training, 0.846 in validation), offering a practical tool for early identification of high-risk patients. Its clinical utility lies in enabling personalized monitoring and timely interventions to improve survival outcomes in this prevalent breast cancer subtype. Introduction Breast cancer remains the most commonly diagnosed malignancy among women worldwide and represents a significant global health burden. The World Health Organization reported that in 2020, breast cancer accounted for approximately 2.3 million new cases and 685,000 deaths worldwide, highlighting the urgent need for effective management strategies to improve patient outcomes. Molecular biology advancements have enabled the classification of breast cancer into distinct subtypes on the basis of hormone receptor status and HER2 expression. These subtypes include luminal A, luminal B, HER2-enriched, and basal-like (triple-negative) breast cancers[3]. Luminal A breast cancer is characterized by estrogen receptor (ER) positivity, progesterone receptor (PR) positivity, HER2 negativity, and low levels of Ki-67 expression[4]. This subtype constitutes approximately 73.5% of breast cancer cases and is typically linked to a favorable prognosis because of its responsiveness to hormonal treatments. However, despite its relatively indolent nature, patients with luminal A breast cancer remain at risk for distant metastases, particularly to the bone[5]. Bone is the predominant site for distant metastasis in approximately 65–75% of patients with metastatic breast cancer[6]. Bone metastases frequently lead to skeletal-related events, negatively impacting patient quality of life and survival [7]. These complications not only decrease overall survival but also contribute to increased healthcare costs and morbidity. Timely identification of high-risk patients for bone metastasis is essential for early intervention and improved clinical outcomes. There are distinct cancer subtypes that manifest differing patterns of metastasis, including luminal A/B tumors that may metastasize to bone, HER2-enriched tumors that may metastasize to the liver and brain, and basal-like tumors that may metastasize to the viscera[8]. Despite this knowledge, current predictive tools for assessing the risk of bone metastasis are limited and often do not account for the unique clinical and biological characteristics of luminal A breast cancer[9]. Most existing models are not tailored to specific molecular subtypes and may lack the accuracy needed for effective risk stratification. There is a clear lack of reliable, large-scale, population-based predictive models specifically designed for predicting bone metastasis in patients with luminal A breast cancer[10]. The early prediction of bone metastasis in these patients is frequently overlooked because of their generally favorable prognosis. However, identifying those at greater risk is essential for implementing personalized monitoring and therapeutic strategies that could improve overall survival and quality of life. The use of comprehensive databases such as the SEER database provides an opportunity to develop a robust predictive model with greater statistical power and generalizability. The SEER program includes cancer incidence and survival data from population-based registries, representing approximately 34.6% of the U.S. population and providing a representative sample for analysis. This study aimed to create a new clinical nomogram using the SEER database to predict bone metastasis risk in luminal A breast cancer patients. This nomogram aims to incorporate readily available clinical parameters to facilitate early identification of high-risk individuals. By focusing on a specific molecular subtype and utilizing a large, authoritative dataset, this model offers innovation in its subtype-specific approach and potential applicability in diverse clinical settings. The model’s predictive performance was assessed by training and validation cohorts from 2010–2013 and from 2014–2015. The nomogram exhibited excellent discrimination ability, with high area under the curve (AUC) values of 0.894 for the training set and 0.846 for the validation set. The development of this nomogram has significant clinical implications. It can aid clinicians in stratifying patients on the basis of their risk of developing bone metastasis, allowing for personalized monitoring strategies and timely therapeutic interventions. Early detection and management of bone metastasis may improve overall survival and enhance the quality of life for these patients. Furthermore, our model provides a foundation for future prospective studies and encourages validation and application across different populations and regions. We aim to enhance Luminal A breast cancer patient care and encourage further research by developing a reliable predictive tool. Data Source Data on breast cancer patients from 2010 to 2015 were obtained from the SEER database of the National Cancer Institute. The SEER database provides extensive population-based data on cancer incidence and survival, sourced from registries representing approximately 34.6% of the U.S. population. Since detailed information on breast cancer subtypes has been available in the SEER database since 2010, this timeframe was selected for analysis. SEER*Stat software version 8.3.5 was used to identify an initial cohort of 345,799 breast cancer cases (https://seer.cancer.gov/seerstat/). For each patient, the extracted variables included age at diagnosis, marital status, race, sex, AJCC stage, tumor size (T stage), lymph node involvement (N stage), bone metastasis status, histological type, tumor laterality, tumor grade, breast cancer molecular subtype, follow-up time, and vital status. Patients with incomplete data on breast cancer subtype, AJCC stage, histological grade, T stage, N stage, or laterality were excluded from the study. Following the application of the inclusion and exclusion criteria, 236,132 patients with luminal A breast cancer were deemed eligible for analysis. Study Design and Statistical Analysis The study population was split into a training cohort and a validation cohort according to the year of diagnosis to create and validate a predictive nomogram for bone metastasis in LABC patients. Patients diagnosed from 2010 to 2013 were included in the training set (n = 152,850), whereas those diagnosed from 2014 to 2015 were included in the validation set (n = 83,282). This division allowed for temporal validation of the nomogram. Initial univariate logistic regression analysis of the training set identified variables significantly linked to bone metastasis. Variables with p values less than 0.05 were deemed statistically significant and were included in the multivariate logistic regression analysis. The multivariate analysis aimed to determine independent predictive factors for bone metastasis among LABC patients. Stepwise model selection was employed, combining statistical criteria and clinical judgment to refine the predictive model. Variables that remained significant in the multivariate analysis and were deemed clinically relevant were included in the final nomogram. The final selection of factors included T stage, N stage, and marital status. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminative ability of the predictive model. The model’s performance was assessed by calculating the area under the ROC curve (AUC) for both the training and validation sets. A 10-fold cross-validation technique was utilized to improve result reliability and mitigate overfitting. Calibration curves were created to assess the model’s calibration by comparing the predicted probabilities of bone metastasis with the observed outcomes. Statistical analyses were conducted via R software version 3.6.2 (https://www.r-project.org/). The Mann‒Whitney U test was employed for continuous variables, and the Pearson chi‒square test was used for categorical variables. The Kaplan‒Meier method was used to estimate overall survival (OS), and group differences were evaluated via the log-rank test. A two-sided p value less than 0.05 was considered statistically significant. Results Metastasis Patterns and Breast Cancer Subtypes in the SEER Database We examined the associations between breast cancer subtypes and metastasis patterns using the SEER database data from 2010 to 2015. As illustrated in Figure 1, the luminal A subtype was the most prevalent subtype, accounting for 73.5% of all cases. This was followed by the basal-like (11.3%), luminal B (10.6%), and HER2-enriched subtypes (4.6%). Among patients with metastases, more than half (51.3%) had bone metastases. Notably, regardless of the site of metastasis, the luminal A subtype remained significantly more common among metastatic patients, accounting for 55.2% of lung metastases, 45.9% of liver metastases, 46% of brain metastases, and 68.6% of bone metastases. Overall Survival Based on Metastasis Sites and Subtypes Among breast cancer patients with metastasis, those with bone metastasis presented the greatest overall survival benefit, whereas patients with brain metastasis presented the least benefit (P < 0.001; Figure 2). Among luminal A breast cancer (LABC) patients, individuals without BMs had significantly better OS than those with BMs did (P < 0.001). Additionally, basal-like breast cancer patients presented significantly worse OS than patients with other subtypes did and across all metastasis sites (P < 0.001). In contrast, luminal B subtype patients had the best OS among all subtypes (P < 0.001). These findings suggest that constructing a nomogram to predict BM in LABC patients could help identify high-risk individuals and potentially improve their survival rates. Patient Characteristics The study flowchart is presented in Figure 3. The study included 236,132 LABC patients from the SEER database from 2010 to 2015. Patients diagnosed between 2010 and 2013 were assigned to the training set (n = 152,850), whereas those diagnosed between 2014 and 2015 formed the validation set (n = 83,282). The collected information included age, marital status, race, sex, AJCC stage, T stage, N stage, BM status, histology, laterality, grade, breast cancer subtype, follow-up time, and vital status. Table 1 provides a summary of the clinical characteristics of both cohorts. The incidence of BM was not significantly different between the training set, with 3,206 patients (2.1%), and the validation set, with 1,794 patients (2.2%) (P = 0.369). The absence of significant differences in clinicopathologic variables between cohorts within both the BM-positive and BM-negative groups supports their suitability as training and validation sets. Identification of Predictive Factors To identify potential predictive factors for BM in LABC patients, we performed univariate logistic regression analyses, followed by multivariate analyses on variables that were significant in the univariate analysis (Table 2). Multivariate analysis revealed strong and significant associations of BM with T stage (OR = 31.86; 95% CI: 28.06–36.21; P < 0.001) and N stage (OR = 4.26; 95% CI: 3.73–4.85; P < 0.001). T stage, N stage, and marital status were identified as key predictive factors through stepwise model selection and clinical judgment. Logistic regression with 10-fold cross-validation was used on both the training and validation sets to assess the discriminative ability of these factors. The three factors demonstrated high predictive performance, with an area under the ROC curve (AUC) of 0.894 (95% CI: 0.890–0.899) in the training set and 0.846 (95% CI: 0.837–0.856) in the validation set (Figure 3). Nomogram Construction and Calibration A nomogram incorporating the three predictive factors from the training cohort was constructed to predict BM risk in LABC patients (Figure 4). The nomogram indicated that the T stage was the most significant factor in predicting BM, followed by the N stage and marital status. Each level of the variables was assigned a specific score, and the total score was calculated by summing these values to estimate the probability of BM. Calibration plots demonstrated strong concordance between the predicted and observed BM rates in both the training and validation sets (Figure 4), validating the nomogram’s reliability. Impact of BM on Overall Survival Across Subgroups We further analyzed the effect of BM on OS among LABC patients stratified by different clinical characteristics (Figure 5). Significant differences in OS were observed across all subgroups (P < 0.001). Notably, patients with early-stage disease (T1 stage and N1 stage), who generally have a better prognosis and a lower incidence of BM, presented higher hazard ratios for mortality when BM was present (OR = 11.47; 95% CI: 10.27–12.68 and OR = 10.6; 95% CI: 9.78–11.42, respectively). Additionally, patients younger than 60 years (OR = 16.15; 95% CI: 15.09–17.21), those who were married (OR = 11.59; 95% CI: 10.85–12.32), nonwhite patients (OR = 11.07; 95% CI: 10.14–12), and female patients (OR = 9.14; 95% CI: 8.77–9.51) had relatively high hazard ratios associated with BM. These findings highlight the significant impact of BM on survival across various patient subgroups. Discussion In this study, we developed and validated a novel clinical nomogram to predict the risk of bone metastasis (BM) in patients with luminal A breast cancer (LABC) with data from the SEER database. The occurrence of BM in LABC patients was significant, with rates of 2.1% in the training cohort and 2.2% in the validation cohort. T stage, N stage, and marital status were identified as significant predictors, each of which was strongly associated with BM risk. The nomogram exhibited strong predictive accuracy, achieving area under the curve (AUC) values of 0.894 in the training set and 0.846 in the validation set, reflecting its robust discrimination ability. The identification of T stage and N stage as significant predictors underscores the importance of tumor burden and lymph node involvement in the progression of LABC to bone metastasis. An advanced T stage reflects a larger tumor size and potentially more aggressive disease biology, increasing the likelihood of tumor cells disseminating to distant sites such as bone[11]. Similarly, a higher N stage indicates greater lymphatic spread, serving as a conduit for metastatic cells to reach systemic circulation and ultimately colonize bone tissue[12]. These findings align with earlier research showing a correlation between advanced tumor stages and a heightened risk of distant metastases. Marital status emerged as an independent predictive factor for BM, which may be attributed to several psychosocial and socioeconomic factors. Married patients might have better social support systems, leading to improved adherence to treatment protocols and earlier detection of disease progression[12]. Conversely, compared with married prostate cancer patients, unmarried patients have a higher mortality rate and shorter survival time. [13]. Our findings align with those of previous studies reporting the incidence of BM in breast cancer patients. Breast cancer subtypes show unique metastasis patterns and prognoses. Luminal A tumors exhibit a higher incidence of bone metastasis than other subtypes do, with rates between 1.4% and 6.2%[14, 15]. HER2-positive and triple-negative breast cancers tend to metastasize more frequently to visceral organs, particularly the liver and lungs[16]. The clinical utility of the nomogram is to enable early detection of LABC patients at elevated risk for BM. By incorporating easily obtainable clinical variables, clinicians can stratify patients and implement personalized monitoring strategies. Early detection of bone metastases (BMs) in cancer patients is crucial for timely intervention and improved outcomes. Bone metastases frequently occur in advanced breast and prostate cancers, resulting in skeletal-related events that adversely affect quality of life and survival [17]. Various imaging modalities and biomarkers can aid in early BM detection[18]. Bone-modifying agents, such as bisphosphonates and denosumab, have shown efficacy in reducing SREs and potentially improving outcomes in certain patient subsets[19, 20]. Additionally, novel treatments such as radium-223 chloride have demonstrated survival benefits in prostate cancer patients with BM[21]. Patient demographics, clinical stage, tumor pathology, and molecular receptor status are risk factors for BM and SREs [22]. Furthermore, the nomogram supports decision-making in allocating resources efficiently, ensuring that high-risk patients receive appropriate attention without overburdening healthcare systems. This study’s strengths lie in the use of a large, population-based dataset from the SEER program, which improves the generalizability and reliability of the findings. The substantial sample size increases the statistical power and reduces the likelihood of random errors. The nomogram’s high predictive performance, indicated by strong AUC values in both the training and validation cohorts, underscores its robustness and potential clinical effectiveness. The innovation of this research lies in developing a predictive model specifically for LABC patients, addressing a gap in the literature and providing a tool tailored to this prevalent breast cancer subtype. Nonetheless, certain limitations should be recognized. As a retrospective study, inherent biases such as selection bias and unmeasured confounding factors may influence the results. The SEER database does not provide comprehensive details on systemic treatments, including chemotherapy regimens, adherence to endocrine therapy, and the use of targeted agents, which may influence BM risk. Lifestyle factors, genetic mutations (e.g., BRCA status), and molecular biomarkers were also not available, potentially affecting the comprehensiveness of the model. Although the nomogram showed strong internal performance, external validation across diverse populations and clinical settings is needed to verify its applicability and generalizability. Future research should focus on prospective, multicenter studies to validate the nomogram externally and assess its practical utility in diverse patient populations. The incorporation of additional clinical variables, such as treatment modalities, genetic profiles, and molecular markers, could increase the predictive accuracy of the model. The incorporation of the nomogram into clinical decision support systems can increase its use in routine practice, assisting clinicians in making informed decisions about surveillance and management strategies for LABC patients. Conclusion This study effectively created a clinically useful nomogram to predict bone metastasis risk in luminal A breast cancer patients via extensive SEER data. The predictive factors identified—T stage, N stage, and marital status—are readily available in clinical settings, increasing the model’s practicality. By enabling early identification of high-risk patients, the nomogram has the potential to improve patient outcomes through timely interventions and personalized care strategies. We advocate for additional research to validate and improve this predictive tool, with the goal of integrating it into clinical practice to enhance patient care and prognosis for those with luminal A breast cancer. Ethical Considerations Given that the SEER database contains deidentified patient information, this study was exempt from institutional review board approval. All procedures adhered to the ethical standards outlined in the Declaration of Helsinki. Funding This study was supported by the Chongqing Municipality Natural Science Foundation (Grant Number: CSTB2022NSCQ-MSX0940). Data Availability Statement The data that support the findings of this study are available from the Surveillance, Epidemiology, and End Results (SEER) Program database (https://seer.cancer.gov/). The data used in this study cover the period from 2010 to 2015. These data are publicly accessible but require submission of a Data Use Agreement to the National Cancer Institute’s SEER Program for access. References 1. Arnold, M., et al., Current and future burden of breast cancer: Global statistics for 2020 and 2040. 2022. 66 : p. 15 - 23.2. Sung, H., et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 2021. 71 : p. 209 - 249.3. Perou, C.M., et al., Molecular portraits of human breast tumours. 2000. 406 (6797): p. 747-752.4. Goldhirsch, A., et al., Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol, 2013. 24 (9): p. 2206-23.5. Kennecke, H., et al., Metastatic behavior of breast cancer subtypes. J Clin Oncol, 2010. 28 (20): p. 3271-7.6. Xu, D. and M.J.T.B.j.o.r. Tang, Advances in the study of biomarkers related to bone metastasis in breast cancer. 2023: p. 20230117.7. Tsuzuki, S., et al., Skeletal complications in cancer patients with bone metastases. 2016. 23 .8. Kunikullaya, S.U., et al., Pattern of distant metastasis in molecular subtypes of carcinoma breast: An institutional study. 2017. 54 1 : p. 327-332.9. Tabor, S., et al., How to Predict Metastasis in Luminal Breast Cancer? Current Solutions and Future Prospects. 2020. 21 .10. Ye, L., et al., Nomogram for predicting the risk of bone metastasis in breast cancer: a SEER population-based study. 2020. 9 : p. 6710 - 6719.11. Yang, S.X., S.M. Hewitt, and J.J.N.P.O. Yu, Locoregional tumor burden and risk of mortality in metastatic breast cancer. 2022. 6 .12. Leong, S.P., et al., The lymphatic system and sentinel lymph nodes: conduit for cancer metastasis. 2021. 39 : p. 139 - 157.13. Guo, Z., et al., Association between Marital Status and Prognosis in Patients with Prostate Cancer: A Meta-Analysis of Observational Studies. 2020.14. Chen, X., et al., Baseline staging tests based on molecular subtype is necessary for newly diagnosed breast cancer. 2014. 33 : p. 28 - 28.15. Shi, D., et al., Predicting the Incidence and Prognosis of Bone Metastatic Breast Cancer: A SEER-Based Observational Study. 2020. 2020 .16. Wei, S. and G.P.J.A.i.a.p. Siegal, Metastatic Organotropism: An Intrinsic Property of Breast Cancer Molecular Subtypes. 2017. 24 2 : p. 78-81.17. Ibrahim, T., L. Mercatali, and D.J.O.L. Amadori, A new emergency in oncology: Bone metastases in breast cancer patients (Review). 2013. 6 : p. 306 - 310.18. Iuliani, M., et al., Current and Emerging Biomarkers Predicting Bone Metastasis Development. 2020. 10 .19. Heeke, A.L., M.R. Nunes, and F.J.C.B.C.R. Lynce, Bone-Modifying Agents in Early-Stage and Advanced Breast Cancer. 2018. 10 : p. 241 - 250.20. Brodowicz, T., et al., Early identification and intervention matters: A comprehensive review of current evidence and recommendations for the monitoring of bone health in patients with cancer. 2017. 61 : p. 23-34.21. Mackiewicz-Wysocka, M., M. Pankowska, and P.J.J.E.O.o.I.D. Wysocki, Progress in the treatment of bone metastases in cancer patients. 2012. 21 : p. 785 - 795.22. Zhang, H., et al., Incidence, risk factors and prognostic characteristics of bone metastases and skeletal-related events (SREs) in breast cancer patients: A systematic review of the real world data. 2018. 11 : p. 38 - 50. Supplementary Material File (fig1.tif) Download 1.40 MB File (fig2.tif) Download 2.10 MB File (fig3.tif) Download 1.61 MB File (fig4.tif) Download 2.03 MB File (fig5.tif) Download 2.78 MB File (table1.docx) Download 17.87 KB File (table2.docx) Download 17.19 KB Information & Authors Information Version history V1 Version 1 13 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Zehao Cai The First Affiliated Hospital of Chongqing Medical University Yubei Hospital View all articles by this author Qiuyan Luo The First Affiliated Hospital of Chongqing Medical University Yubei Hospital View all articles by this author Yong Fu Dianjiang People’s Hospital of Chongqing View all articles by this author Han Li The First Affiliated Hospital of Chongqing Medical University Yubei Hospital View all articles by this author shengchun liu 0000-0002-9933-3643 The First Affiliated Hospital of Chongqing Medical University Yubei Hospital View all articles by this author Yang Peng 0000-0002-4146-8369 [email protected] The First Affiliated Hospital of Chongqing Medical University Yubei Hospital View all articles by this author Metrics & Citations Metrics Article Usage 123 views 73 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Zehao Cai, Qiuyan Luo, Yong Fu, et al. Development of a new clinical nomogram to predict bone metastasis in luminal A breast cancer patients: A SEER-based study. Authorea . 13 March 2025. DOI: https://doi.org/10.22541/au.174186569.92563323/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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