Enhancing Breast Cancer Recurrence Prediction Across Treatment Scenarios with Weighted Cox Mixtures

Breast cancer treatment involves surgery, radiation, chemotherapy, and endocrine therapy, with recurrence risk depending on treatment execution. We propose a weighted Cox mixtures model that integrates treatment plans and clinical data to estimate recurrence risk. Data from Mayo Clinic (US) and the National Institute of Oncology (Morocco) inform the model. We enhance expectation maximization within the Cox mixtures model using three weighting strategies: Inverse Probability of Treatment Weighting, Adaptive Weights with focal loss, and Prioritizing Subgroups. In the Mayo Clinic cohort, Adaptive Weights improve predictive accuracy (C-index: 0.67-0.88), outperforming the standard Cox model. In the Moroccan cohort, Adaptive Weights also enhance C-index values (0.60-0.71), though with larger confidence intervals. Our findings demonstrate that weighting strategies refine recurrence risk prediction, particularly in imbalanced cohorts. Expanding datasets, especially in underrepresented populations, is crucial for improving model reliability and clinical applicability.