A recent study published on Nature demonstrates how explainable machine learning—in particular, Gradient Boosting and SHAP methods—can differentiate survival outcomes between mastectomy and breast conserving surgeries. By analyzing key factors such as relapse-free status and age, the research highlights potential for personalized treatment. These findings, derived from the METABRIC dataset, provide valuable insights for clinical decision-making in oncology.