Prediction of Overall and Relapse-Free Survival in Triple-Negative Breast Cancer Patients Through Machine Learning-Based Clustering on Clinical Data. (2025)

[INTRODUCTION] Triple-negative breast cancer (TNBC) accounts for 15% to 20% of breast cancer cases and is characterized by its aggressiveness and high relapse rate. Due to the absence of hormonal receptors and HER2, standard treatment relies on chemotherapy, yielding limited outcomes in overall survival (OS) and relapse-free survival (RFS). The molecular heterogeneity of TNBC complicates risk stratification and personalized treatment approaches. In this context, unsupervised machine learning could improve the identification of clinically homogeneous subgroups and facilitate prognostic predictions.

[OBJECTIVE] To develop predictive models for OS and RFS in TNBC patients using machine learning algorithms, specifically k-prototypes for subgroup identification and random forest for outcome pr