Interpretable machine learning models for oncotypedDX® prediction using clinicopathologic data: a brazilian cohort study

Oncotype DX® (ODX) guides adjuvant chemotherapy decisions in early breast cancer (eBC), but its high-cost limits access in resource-constrained settings. We developed and evaluated an interpretable machine learning model using routinely available clinicopathologic variables to predict ODX recurrence score (RS) and optimize genomic testing. We analyzed 935 Brazilian patients with eBC from the GBECAM cohort (2005–2024). Ridge regression was selected for its predictive performance, interpretability, and computational efficiency. Patients were stratified into risk groups based on predicted probability of RS > 25, and recurrence-free survival was estimated using the Kaplan–Meier method. The model showed good discrimination for identifying RS > 25 (AUROC 0.818 [95% CI, 0.780–0.852]), with sensitivity of 0.556 and specificity of 0.859. The highest-risk quartile contained 60.8% of all RS > 25 cases (observed probability 42.8%), whereas the lowest-risk half had an observed probability of only 4.4%. These findings suggest that up to 75% of patients could be triaged without confirmatory ODX testing, reserving genomic assays for an intermediate-risk subgroup. The Ridge model outperformed previously published clinicopathologic models and may represent a cost-effective strategy to improve ODX testing prioritization and resource allocation.