Predicting breast cancer-specific survival in metaplastic breast cancer patients using machine learning algorithms

Reis-Filho, 2019, Metaplastic carcinoma Tan, 2020, The 2019 World Health Organization classification of tumours of the breast, Histopathology., 77, 181, 10.1111/his.14091 Weigelt, 2014, Metaplastic breast carcinoma: more than a special type, Nat Rev Cancer., 14, 147, 10.1038/nrc3637 Hennessy, 2009, Characterization of a naturally occurring breast cancer subset enriched in epithelial-to-mesenchymal transition and stem cell characteristics, Cancer Res., 69, 4116, 10.1158/0008-5472.CAN-08-3441 McCart Reed, 2019, Phenotypic and molecular dissection of metaplastic breast cancer and the prognostic implications, J Pathol., 247, 214, 10.1002/path.5184 Al-Hilli, 2019, Metaplastic breast cancer has a poor response to neoadjuvant systemic therapy, Breast Cancer Res Treat., 176, 709, 10.1007/s10549-019-05264-2 Ghosh, 2017, Metaplastic carcinoma breast: a clinical analysis of nine cases, J Clin Diagn Res JCDR., 11, XR01 Reddy, 2020, A comprehensive overview of metaplastic breast cancer: clinical features and molecular aberrations, Breast Cancer Res., 22, 121, 10.1186/s13058-020-01353-z Lee, 2023, Clinical characteristics and prognosis of metaplastic breast cancer compared with invasive ductal carcinoma: a propensity-matched analysis, Cancers., 15, 1556, 10.3390/cancers15051556 Thomas, 2023, Metaplastic breast cancer: a review, Crit Rev Oncol Hematol., 182, 10.1016/j.critrevonc.2023.103924 Lee, 2021, Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database, Lancet Digit Health., 3, e158, 10.1016/S2589-7500(20)30314-9 Gupta, 2014, Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry, BMJ Open., 4, 10.1136/bmjopen-2013-004007 Parikh, 2019, Machine learning approaches to predict 6-month mortality among patients with cancer, JAMA Netw Open., 2, 10.1001/jamanetworkopen.2019.15997 Kourou, 2015, Machine learning applications in cancer prognosis and prediction, Comput Struct Biotechnol J., 13, 8, 10.1016/j.csbj.2014.11.005 Cruz, 2007, Applications of machine learning in cancer prediction and prognosis, Cancer Inform., 2, 59 Kong, 2022, Network-based machine learning approach to predict immunotherapy response in cancer patients, Nat Commun., 13, 3703, 10.1038/s41467-022-31535-6 Corso, 2021, Metaplastic breast cancer: prognostic and therapeutic considerations, J Surg Oncol., 123, 61, 10.1002/jso.26248 Qiu, 2022, Differences of clinicopathological features between metaplastic breast carcinoma and nonspecific invasive breast carcinoma and prognostic profile of metaplastic breast carcinoma, Breast J., 2022, 10.1155/2022/2500594 Kalaw, 2020, Metaplastic breast cancers frequently express immune checkpoint markers FOXP3 and PD-L1, Br J Cancer., 123, 1665, 10.1038/s41416-020-01065-3 Salgado, 2015, The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014, Ann Oncol Off J Eur Soc Med Oncol., 26, 259, 10.1093/annonc/mdu450 van Buuren, 2018 Wickham, 2009 Thapa B, Arobelidze S, Clark BA, et al. Metaplastic breast cancer: characteristics and survival outcomes. Cureus. 14(8):e28551. https://doi.org/10.7759/cureus.28551 Green, 2016, Nottingham Prognostic Index Plus: validation of a clinical decision making tool in breast cancer in an independent series, J Pathol Clin Res., 2, 32, 10.1002/cjp2.32 Moukarzel, 2021, The genetic landscape of metaplastic breast cancers and uterine carcinosarcomas, Mol Oncol., 15, 1024, 10.1002/1878-0261.12813 Chen, 2016, Effect of age on breast cancer patient prognoses: a population-based study using the SEER 18 database, PLoS ONE., 11, 10.1371/journal.pone.0165409 Han, 2004, Young age: an independent risk factor for disease-free survival in women with operable breast cancer, BMC Cancer., 4, 82, 10.1186/1471-2407-4-82 Derks, 2018, Impact of age on breast cancer mortality and competing causes of death at 10 years follow-up in the adjuvant TEAM trial, Eur J Cancer Oxf Engl 1990, 99, 1 Zhang, 2015, Impact of rhabdoid differentiation on prognosis for patients with grade 4 renal cell carcinoma, Eur Urol., 68, 5, 10.1016/j.eururo.2015.01.002 Zhang, 2015, Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer, Sci Rep., 5, 16066, 10.1038/srep16066 Drasin, 2011, Breast cancer epithelial-to-mesenchymal transition: examining the functional consequences of plasticity, Breast Cancer Res., 13, 226, 10.1186/bcr3037 Özkurt, 2022, Metaplastic breast cancer: mesenchymal subtype has worse survival outcomes, Breast Care Basel Switz., 17, 554, 10.1159/000525324 van Seijen, 2019, Ductal carcinoma in situ: to treat or not to treat, that is the question, Br J Cancer., 121, 285, 10.1038/s41416-019-0478-6 Neri, 2005, Prognostic value of extracapsular extension of axillary lymph node metastases in T1 to T3 breast cancer, Ann Surg Oncol., 12, 246, 10.1245/ASO.2005.02.029 Rajappa, 2019, Extracapsular extension in oral cavity cancers—predictive factors and impact on recurrence pattern and survival, Int J Oral Maxillofac Surg., 48, 989, 10.1016/j.ijom.2019.03.900 Krstevska, 2015, Evolution of treatment and high-risk features in resectable locally advanced head and neck squamous cell carcinoma with special reference to extracapsular extension of nodal disease, J BUON Off J Balk Union Oncol., 20, 943 Nappi, 2008, Prognostic and predictive factors of breast carcinoma: beyond hormonal receptors and HER2, Eur J Cancer Suppl., 6, 1, 10.1016/j.ejcsup.2008.06.011 Thomas, 2000, Prognostic and predictive implications of HER2 status for breast cancer patients, Eur J Oncol Nurs., 4, 10, 10.1054/ejon.2000.0073 Karsch-Bluman, 2019, Tissue necrosis and its role in cancer progression, Oncogene., 38, 1920, 10.1038/s41388-018-0555-y Proskuryakov, 2010, Mechanisms of tumor cell necrosis, Curr Pharm Des., 16, 56, 10.2174/138161210789941793 Ling, 2020, Tumor necrosis as a poor prognostic predictor on postoperative survival of patients with solitary small hepatocellular carcinoma, BMC Cancer., 20, 607, 10.1186/s12885-020-07097-5 Valenza, 2023, Tumor infiltrating lymphocytes across breast cancer subtypes: current issues for biomarker assessment, Cancers., 15, 767, 10.3390/cancers15030767 Nelson, 2021, Prognostic and therapeutic role of tumor-infiltrating lymphocyte subtypes in breast cancer, Cancer Metastasis Rev., 40, 519, 10.1007/s10555-021-09968-0 Farhangfar, 2013, The impact of tumor heterogeneity on patient treatment decisions, Clin Chem., 59, 38, 10.1373/clinchem.2012.194712 Yang, 2017, The value of positive lymph nodes ratio combined with negative lymph node count in prediction of breast cancer survival, J Thorac Dis., 9, 10.21037/jtd.2017.05.30 Roberts, 2017, Breast cancer-specific survival in patients with lymph node-positive hormone receptor-positive invasive breast cancer and Oncotype DX Recurrence Score results in the SEER database, Breast Cancer Res Treat., 163, 303, 10.1007/s10549-017-4162-3 Carter, 1989, Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases, Cancer., 63, 181, 10.1002/1097-0142(19890101)63:1<181::AID-CNCR2820630129>3.0.CO;2-H He, 2019, Prognosis in different subtypes of metaplastic breast cancer: a population-based analysis, Breast Cancer Res Treat., 173, 329, 10.1007/s10549-018-5005-6 Jung, 2010, Worse prognosis of metaplastic breast cancer patients than other patients with triple-negative breast cancer, Breast Cancer Res Treat., 120, 627, 10.1007/s10549-010-0780-8 Tonellotto, 2019, Impact of number of positive lymph nodes and lymph node ratio on survival of women with node-positive breast cancer, Eur J Breast Health., 15, 76, 10.5152/ejbh.2019.4414 Wu, 2023, Using machine learning for mortality prediction and risk stratification in atezolizumab-treated cancer patients: integrative analysis of eight clinical trials, Cancer Med., 12, 3744, 10.1002/cam4.5060 Ayer, 2010, Breast cancer risk estimation with artificial neural networks revisited, Cancer., 116, 3310, 10.1002/cncr.25081 Silva, 2023, Machine learning for longitudinal mortality risk prediction in patients with malignant neoplasm in São Paulo, Brazil, Artif Intell Life Sci., 3 Arumugam K, Naved M, Shinde PP, Leiva-Chauca O, Huaman-Osorio A, Gonzales-Yanac T. Multiple disease prediction using machine learning algorithms. Mater Today Proc. Published online August 2, 2021. https://doi.org/10.1016/j.matpr.2021.07.361 Shaikh, 2022, Prediction of cancer disease using machine learning approach, Mater Today Proc., 50, 40, 10.1016/j.matpr.2021.03.625 Kim, 2010, Decision tree of occupational lung cancer using classification and regression analysis, Saf Health Work., 1, 140, 10.5491/SHAW.2010.1.2.140 Hastie, 2001 Smith, 2014, Correcting for optimistic prediction in small data sets, Am J Epidemiol., 180, 318, 10.1093/aje/kwu140 Islam, 2023, Chronic kidney disease prediction based on machine learning algorithms, J Pathol Inform., 14, 10.1016/j.jpi.2023.100189 Jung, 2018, Multiple predicting K-fold cross-validation for model selection, J Nonparametric Stat., 30, 197, 10.1080/10485252.2017.1404598 Zhou, 2021, A machine learning-based predictor for the identification of the recurrence of patients with gastric cancer after operation, Sci Rep., 11, 1571, 10.1038/s41598-021-81188-6 Li, 2021, Predicting breast cancer 5-year survival using machine learning: A systematic review, PLoS ONE., 16, 10.1371/journal.pone.0250370 Lopez, 2020, PTEN expression as a complementary biomarker for mismatch repair testing in breast cancer, Int J Mol Sci., 21, 1461, 10.3390/ijms21041461 Yndestad, 2017, High PTEN gene expression is a negative prognostic marker in human primary breast cancers with preserved p53 function, Breast Cancer Res Treat., 163, 177, 10.1007/s10549-017-4160-5 Li, 2017, Loss of PTEN expression in breast cancer: association with clinicopathological characteristics and prognosis, Oncotarget., 8, 32043, 10.18632/oncotarget.16761 Sajjadi, 2021, Combined analysis of PTEN, HER2, and hormone receptors status: remodeling breast cancer risk profiling, BMC Cancer., 21, 1152, 10.1186/s12885-021-08889-z Carbognin, 2019, Prognostic and predictive implications of PTEN in breast cancer: unfulfilled promises but intriguing perspectives, Cancers., 11, 1401, 10.3390/cancers11091401