[1] J. Huang, P.S. Chan, V. Lok, X. Chen, H. Ding, Y. Jin, J. Yuan, X.Q. Lao, Z.J. Zheng, M.C. Wong. Global incidence and mortality of breast cancer: a trend analysis. Aging (Albany NY) 13 (2021) 5748-5803. https://doi.org/10.18632/aging.202502 DOI: https://doi.org/10.18632/aging.202502

[2] F. Lumachi, G. Luisetto, S. Mm Basso, U. Basso, A. Brunello, V. Camozzi. Endocrine therapy of breast cancer. Current medicinal chemistry 18 (2011) 513-522. https://doi.org/10.2174/092986711794480177 DOI: https://doi.org/10.2174/092986711794480177

[3] A. Howell, S.J. Howell. Tamoxifen evolution. British Journal of Cancer 128 (2023) 421-425. https://doi.org/10.1038/s41416-023-02158-5 DOI: https://doi.org/10.1038/s41416-023-02158-5

[4] S. Manna, M.K. Holz. Tamoxifen action in ER-negative breast cancer. Signal transduction insights 5 (2016) STI-29901. https://doi.org/10.4137/sti.s29901 DOI: https://doi.org/10.4137/STI.S29901

[5] Early Breast Cancer Trialists' Collaborative Group. Aromatase inhibitors versus tamoxifen in early breast cancer: patient-level meta-analysis of the randomised trials. The Lancet 386 (2015) 1341-1352. https://doi.org/10.1016/s0140-6736(15)61074-1 DOI: https://doi.org/10.1016/S0140-6736(15)61074-1

[6] Y. Yang, W. Pan, X. Tang, S. Wu, X. Sun. A meta-analysis of randomized controlled trials comparing the efficacy and safety of anastrozole versus tamoxifen for breast cancer. Oncotarget 8 (2017) 48362-48374. https://doi.org/10.18632/oncotarget.16466 DOI: https://doi.org/10.18632/oncotarget.16466

[7] W. Lorizio, A.H. Wu, M.S. Beattie, H. Rugo, S. Tchu, K. Kerlikowske, E. Ziv. Clinical and biomarker predictors of side effects from tamoxifen. Breast cancer research and treatment 132 (2012) 1107-1118. https://doi.org/10.1007/s10549-011-1893-4 DOI: https://doi.org/10.1007/s10549-011-1893-4

[8] J.M. Hoskins, L.A. Carey, H.L. McLeod. CYP2D6 and tamoxifen: DNA matters in breast cancer. Nature Reviews Cancer 9 (2009) 576-586. https://doi.org/10.1038/nrc2683 DOI: https://doi.org/10.1038/nrc2683

[9] Z. Zeng, Y. Liu, Z. Liu, J. You, Z. Chen, J. Wang, Q. Peng, L. Xie, R. Li, S. Li, X. Qin. CYP2D6 polymorphisms influence tamoxifen treatment outcomes in breast cancer patients: a meta-analysis. Cancer chemotherapy and pharmacology 72 (2013) 287-303. https://doi.org/10.1007/s00280-013-2195-9 DOI: https://doi.org/10.1007/s00280-013-2195-9

[10] M.E. Ozer, P.O. Sarica, K.Y. Arga. New machine learning applications to accelerate personalized medicine in breast cancer: rise of the support vector machines. Omics: a journal of integrative biology 24 (2020) 241-246. https://doi.org/10.1089/omi.2020.0001 DOI: https://doi.org/10.1089/omi.2020.0001

[11] C. Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence 1 (2019) 206-215. https://doi.org/10.1038/s42256-019-0048-x DOI: https://doi.org/10.1038/s42256-019-0048-x

[12] A. Aravindkumar, M. Ramadoss, S.A. Fakhruddin Ahmed, V. Sampath, K. Lakshminarayanan. Explainable AI in healthcare: a systematic review of XAI use cases in imaging, diagnostics, and rehabilitation. Front Artif Intell 9 (2026) 1749527. https://doi.org/10.3389/frai.2026.1749527 DOI: https://doi.org/10.3389/frai.2026.1749527

[13] M. A. Province, M.P. Goetz, H. Brauch, D.A. Flockhart, J.M. Hebert, R. Whaley, V.J. Suman, W. Schroth, S. Winter, H. Zembutsu, T. Mushiroda. CYP2D6 genotype and adjuvant tamoxifen: meta‐analysis of heterogeneous study populations. Clinical Pharmacology & Therapeutics 95 (2014) 216-227. https://doi.org/10.1038/clpt.2013.186 DOI: https://doi.org/10.1038/clpt.2013.186

[14] R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.R-project.org/ (Accessed on 27 April 2026).

[15] C. Verschraegen, V. Vinh-Hung, G. Cserni, R. Gordon, M.E. Royce, G. Vlastos, P. Tai, G. Storme. Modeling the effect of tumor size in early breast cancer. Annals of surgery 241 (2005) 309-318. https://doi.org/10.1097/01.sla.0000150245.45558.a9 DOI: https://doi.org/10.1097/01.sla.0000150245.45558.a9

[16] A.W. Fyles, D.R. McCready, L.A. Manchul, M.E. Trudeau, P. Merante, M. Pintilie, L.M. Weir, I.A. Olivotto. Tamoxifen with or without breast irradiation in women 50 years of age or older with early breast cancer. New England Journal of Medicine 351 (2004) 963-70. https://doi.org/10.1056/nejmoa040595 DOI: https://doi.org/10.1056/NEJMoa040595

[17] K.A. Bertrand, R.M. Tamimi, C.G. Scott, M.R. Jensen, V.S. Pankratz, D. Visscher, A. Norman, F. Couch, J. Shepherd, B. Fan, Y.Y. Chen. Mammographic density and risk of breast cancer by age and tumor characteristics. Breast Cancer Research 15 (2013) R104. https://doi.org/10.1186/bcr3570 DOI: https://doi.org/10.1186/bcr3570

[18] J. Cuzick, J. Warwick, E. Pinney, R.M. Warren, S.W. Duffy. Tamoxifen and breast density in women at increased risk of breast cancer. Journal of the National Cancer Institute 96 (2004) 621-628. https://doi.org/10.1093/jnci/djh106 DOI: https://doi.org/10.1093/jnci/djh106

[19] C. Owusu, D.S. Buist, T.S. Field, T.L. Lash, S.S. Thwin, A.M. Geiger, V.P. Quinn, F. Frost, M. Prout, M.U. Yood, F. Wei. Predictors of tamoxifen discontinuation among older women with estrogen receptor–positive breast cancer. Journal of Clinical Oncology 26 (2008) 549-55. https://doi.org/10.1200/jco.2006.10.1022 DOI: https://doi.org/10.1200/JCO.2006.10.1022

[20] S.B. Wheeler, J. Spencer, L.C. Pinheiro, C.C. Murphy, J.A. Earp, L. Carey, A. Olshan, C.K. Tse, M.E. Bell, M. Weinberger, K.E. Reeder-Hayes. Endocrine therapy nonadherence and discontinuation in black and white women. JNCI: Journal of the National Cancer Institute 111 (2019) 498-508. https://doi.org/10.1093/jnci/djy136 DOI: https://doi.org/10.1093/jnci/djy136

[21] H.M. Johnson, H. Shivalingappa, W. Irish, J.H. Wong, M. Muzaffar, K. Verbanac, N.A. Vohra. Race May Not Impact Endocrine Therapy–Related Changes in Breast Density. Cancer Epidemiology, Biomarkers & Prevention 29 (2020) 1049-57. https://doi.org/10.1158/1055-9965.epi-19-1066 DOI: https://doi.org/10.1158/1055-9965.EPI-19-1066

[22] D.P. Cronin-Fenton, P. Damkier. Tamoxifen and CYP2D6: a controversy in pharmacogenetics. Advances in pharmacology 83 (2018) 65-91. https://doi.org/10.1016/bs.apha.2018.03.001 DOI: https://doi.org/10.1016/bs.apha.2018.03.001

[23] W. Schroth, M.P. Goetz, U. Hamann, P.A. Fasching, M. Schmidt, S. Winter, P. Fritz, W. Simon, V.J. Suman, M.M. Ames, S.L. Safgren. Association between CYP2D6 polymorphisms and outcomes among women with early stage breast cancer treated with tamoxifen. JAMA 302 (2009) 1429-1436. https://doi.org/10.1001/jama.2009.1420 DOI: https://doi.org/10.1001/jama.2009.1420

[24] R.D. Riley, G.S. Collins, L. Kirton, K.I. Snell, J. Ensor, R. Whittle, P. Dhiman, M. Van Smeden, X. Liu, J. Alderman, K. Nirantharakumar. Uncertainty of risk estimates from clinical prediction models: rationale, challenges, and approaches. BMJ 388 (2025) e080749. https://doi.org/10.1136/bmj-2024-080749 DOI: https://doi.org/10.1136/bmj-2024-080749

[25] Y. Zhang, Y. Weng, J. Lund. Applications of explainable artificial intelligence in diagnosis and surgery. Diagnostics 12 (2022) 237. https://doi.org/10.3390/diagnostics12020237 DOI: https://doi.org/10.3390/diagnostics12020237

[26] T. Hulsen. Explainable artificial intelligence (XAI): concepts and challenges in healthcare. AI 4 (2023) 652-666. https://doi.org/10.3390/ai4030034 DOI: https://doi.org/10.3390/ai4030034

[27] Y. Yang, X. Liu. Application of explainable artificial intelligence integrating with electronic health record in oncology. Exploration of Targeted Anti-Tumor Therapy 7 (2026) 1002357. https://doi.org/10.37349/etat.2026.1002357 DOI: https://doi.org/10.37349/etat.2026.1002357