{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T10:34:27Z","timestamp":1787567667998,"version":"build-2736575974"},"reference-count":40,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:00:00Z","timestamp":1787443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: Breast cancer is the most common malignancy among women worldwide and a leading cause of cancer-related mortality. Despite advances in diagnosis and treatment, accurately predicting 5-year survival remains challenging because of disease heterogeneity. This study systematically evaluated the performance of machine learning (ML) models for predicting 5-year breast cancer survival and synthesised their overall discriminative performance. Methods: A systematic search of PubMed, Scopus, and Web of Science (2010\u20132024) identified studies developing ML models for 5-year survival prediction. Study quality was assessed using PROBAST. Logit-transformed area under the receiver operating characteristic curve (AUC) estimates were quantitatively synthesised using a random-effects meta-analysis with the restricted maximum likelihood (REML) estimator. Heterogeneity was assessed using Cochran\u2019s Q and I2 statistics, publication bias using funnel plots and Egger\u2019s regression test, and subgroup analyses according to study characteristics. Statistical analyses were performed using IBM SPSS version 29.0 and R version 4.6.1. Results: Fifteen studies were included. A wide range of ML models, including Random Forest, Random Survival Forest, and gradient boosting methods, was evaluated. The pooled analysis demonstrated good discriminative performance, with an overall AUC of 0.83 (95% CI: 0.80\u20130.86). Substantial heterogeneity was observed across studies. Ensemble-based models generally showed consistent performance. Publication bias was detected, and several studies exhibited moderate-to-high risk of bias. Conclusions: ML models show strong potential for predicting 5-year breast cancer survival and may support early risk stratification and clinical decision-making. However, greater methodological standardisation, rigorous external validation, and improved reporting are required before widespread clinical implementation.<\/jats:p>","DOI":"10.3390\/cancers18172736","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T09:20:02Z","timestamp":1787563202000},"page":"2736","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating the Performance of Machine Learning Models for Predicting 5-Year Breast Cancer Survival: A Systematic Review and Meta-Analysis"],"prefix":"10.3390","volume":"18","author":[{"given":"Ashin Krishna","family":"Chalil","sequence":"first","affiliation":[{"name":"Division of Medical Statistics, School of Life Sciences, JSS Academy of Higher Education and Research, Mysuru 570015, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0304-3291","authenticated-orcid":false,"given":"Bindu","family":"Therayangalath","sequence":"additional","affiliation":[{"name":"Department of Cancer Registry & Epidemiology, Malabar Cancer Centre, Postgraduate Institute of Oncology Science & Research, Kannur 670103, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vikram","family":"Patil","sequence":"additional","affiliation":[{"name":"Department of Radio Diagnosis, JSS Medical College & Hospital, JSS Academy of Higher Education and Research, Mysuru 570015, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaithra","family":"Nagaraju","sequence":"additional","affiliation":[{"name":"Division of Medical Statistics, School of Life Sciences, JSS Academy of Higher Education and Research, Mysuru 570015, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,23]]},"reference":[{"key":"ref_1","first-page":"438","article-title":"Breast cancer statistics, 2019","volume":"69","author":"DeSantis","year":"2019","journal-title":"CA A Cancer J. Clin."},{"key":"ref_2","first-page":"394","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA A Cancer J. Clin."},{"key":"ref_3","first-page":"209","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA A Cancer J. Clin."},{"key":"ref_4","unstructured":"International Agency for Research on Cancer (IARC) (2020). GLOBOCAN 2020: New Global Cancer Data, World Health Organization. Available online: https:\/\/gco.iarc.fr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/ajco.12661","article-title":"Epidemiology of breast cancer in Indian women","volume":"13","author":"Malvia","year":"2017","journal-title":"Asia-Pac. J. Clin. Oncol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Collaborative Group on Hormonal Factors in Breast Cancer (2001). Familial breast cancer: Collaborative reanalysis of individual data from 52 epidemiological studies including 58 209 women with breast cancer and 101 986 women without the disease. Lancet, 358, 1389\u20131399.","DOI":"10.1016\/S0140-6736(01)06524-2"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S101","DOI":"10.1016\/j.canep.2014.12.007","article-title":"European code against cancer 4th edition: Breastfeeding and cancer","volume":"39","author":"Scoccianti","year":"2015","journal-title":"Cancer Epidemiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/S1470-2045(10)70273-4","article-title":"Breast-cancer early detection in low-income and middle-income countries: Do what you can versus one size fits all","volume":"12","author":"Harford","year":"2011","journal-title":"Lancet Oncol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"310","DOI":"10.6004\/jnccn.2018.0012","article-title":"Breast cancer, version 4.2017, NCCN clinical practice guidelines in oncology","volume":"16","author":"Gradishar","year":"2018","journal-title":"J. Natl. Compr. Cancer Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1126\/science.3798106","article-title":"Human breast cancer: Correlation of relapse and survival with amplification of the HER-2\/neu oncogene","volume":"235","author":"Slamon","year":"1987","journal-title":"Science"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1158\/1078-0432.CCR-16-3001","article-title":"Breast cancer immunotherapy: Facts and hopes","volume":"24","author":"Emens","year":"2018","journal-title":"Clin. Cancer Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4","article-title":"Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors","volume":"15","author":"Harrell","year":"1996","journal-title":"Stat. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","article-title":"Machine learning applications in cancer prognosis and prediction","volume":"13","author":"Kourou","year":"2015","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","article-title":"A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models","volume":"110","author":"Christodoulou","year":"2019","journal-title":"J. Clin. Epidemiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1002\/bjs.9736","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement","volume":"102","author":"Collins","year":"2015","journal-title":"J. Br. Surg."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, J., Zhou, Z., Dong, J., Fu, Y., Li, Y., Luan, Z., and Peng, X. (2021). Predicting breast cancer 5-year survival using machine learning: A systematic review. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0250370"},{"key":"ref_18","first-page":"372","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"29","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.7326\/M18-1377","article-title":"PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration","volume":"170","author":"Moons","year":"2019","journal-title":"Ann. Intern. Med."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dubey, S., Tiwari, G., Singh, S., Goldberg, S., and Pinsky, E. (2023). Using machine learning for healthcare treatment planning. Front. Artif. Intell., 6.","DOI":"10.3389\/frai.2023.1124182"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"30147","DOI":"10.1038\/s41598-024-81734-y","article-title":"Application of machine learning in breast cancer survival prediction using a multimethod approach","volume":"14","author":"Hamedi","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, K., Zhang, J., Yu, Y., Lin, Y., and Song, C. (2022). The impact of chemotherapy and survival prediction by machine learning in early Elderly Triple Negative Breast Cancer (eTNBC): A population based study from the SEER database. BMC Geriatr., 22.","DOI":"10.1186\/s12877-022-02936-5"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Yu, Y., Yu, X., Huang, M., Wang, Q., Huang, K., and Song, C. (2024). Predictive model of prognosis index for invasive micropapillary carcinoma of the breast based on machine learning: A SEER population-based study. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02669-y"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1186\/s40001-023-01361-7","article-title":"Development and testing of a random forest-based machine learning model for predicting events among breast cancer patients with a poor response to neoadjuvant chemotherapy","volume":"28","author":"Jin","year":"2023","journal-title":"Eur. J. Med. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, C., Liu, M., Li, J., Wang, W., Feng, C., Cai, Y., Wu, F., Zhao, X., Du, C., and Zhang, Y. (2022). Machine learning predicts the prognosis of breast cancer patients with initial bone metastases. Front. Public Health, 10.","DOI":"10.3389\/fpubh.2022.1003976"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.62347\/OJTY4008","article-title":"Development and validation of a random survival forest model for predicting long-term survival of early-stage young breast cancer patients based on the SEER database and an external validation cohort","volume":"14","author":"Li","year":"2024","journal-title":"Am. J. Cancer Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/TBME.2020.2993278","article-title":"Optimizing survival analysis of XGBoost for ties to predict disease progression of breast cancer","volume":"68","author":"Liu","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4063","DOI":"10.1111\/cas.15917","article-title":"Machine learning approaches for predicting 5-year breast cancer survival: A multicenter study","volume":"114","author":"Nguyen","year":"2023","journal-title":"Cancer Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Qu, J., Li, C., Liu, M., Wang, Y., Feng, Z., Li, J., Wang, W., Wu, F., Zhang, S., and Zhao, X. (2023). Prognostic models using machine learning algorithms and treatment outcomes of occult breast cancer patients. J. Clin. Med., 12.","DOI":"10.3390\/jcm12093097"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"22055","DOI":"10.1038\/s41598-024-72385-0","article-title":"Developing machine learning models for personalized treatment strategies in early breast cancer patients undergoing neoadjuvant systemic therapy based on SEER database","volume":"14","author":"Ren","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.21037\/gs-24-106","article-title":"Prognostic prediction of breast cancer patients using machine learning models: A retrospective analysis","volume":"13","author":"Song","year":"2024","journal-title":"Gland Surg."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wu, R., Luo, J., Wan, H., Zhang, H., Yuan, Y., Hu, H., Feng, J., Wen, J., Wang, Y., and Li, J. (2023). Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0280340"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e33440","DOI":"10.2196\/33440","article-title":"The application and comparison of machine learning models for the prediction of breast cancer prognosis: Retrospective cohort study","volume":"10","author":"Xiao","year":"2022","journal-title":"JMIR Med. Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1097\/CM9.0000000000002056","article-title":"Multicenter study of the clinicopathological features and recurrence risk prediction model of early-stage breast cancer with low-positive human epidermal growth factor receptor 2 expression in China (Chinese Society of Breast Surgery 021)","volume":"135","author":"Xin","year":"2022","journal-title":"Chin. Med. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"10659","DOI":"10.1007\/s00432-023-04967-w","article-title":"Predictive value of machine learning for breast cancer recurrence: A systematic review and meta-analysis","volume":"149","author":"Lu","year":"2023","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huang, Y., Bazzazzadehgan, S., Li, J., Arabshomali, A., Li, M., Bhattacharya, K., and Bentley, J.P. (2025). Comparison of machine learning methods versus traditional Cox regression for survival prediction in cancer using real-world data: A systematic literature review and meta-analysis. BMC Med. Res. Methodol., 25.","DOI":"10.1186\/s12874-025-02694-z"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Javanmard, Z., Zarean Shahraki, S., Safari, K., Omidi, A., Raoufi, S., Rajabi, M., Akbari, M.E., and Aria, M. (2025). Artificial intelligence in breast cancer survival prediction: A comprehensive systematic review and meta-analysis. Front. Oncol., 14.","DOI":"10.3389\/fonc.2024.1420328"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1038\/s41746-025-01483-8","article-title":"Systematic review and meta-analysis of artificial intelligence in classifying HER2 status in breast cancer immunohistochemistry","volume":"8","author":"Albuquerque","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fu, Y., Zhou, J., and Li, J. (2024). Diagnostic performance of ultrasound-based artificial intelligence for predicting key molecular markers in breast cancer: A systematic review and meta-analysis. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0303669"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hsu, H., Lee, K.H., Karmakar, R., Mukundan, A., Attar, R.S., Liu, P.H., and Wang, H.C. (2025). From Innovation to Application: Can Emerging Imaging Techniques Transform Breast Cancer Diagnosis?. Diagnostics, 15.","DOI":"10.3390\/diagnostics15212718"}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/17\/2736\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T09:32:30Z","timestamp":1787563950000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/17\/2736"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,23]]},"references-count":40,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["cancers18172736"],"URL":"https:\/\/doi.org\/10.3390\/cancers18172736","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,23]]}}}