Development and validation of an interpretable machine learning model for postoperative radiotherapy decision-making in ypN0 breast cancer after neoadjuvant chemotherapy: a real-world study

We developed and validated a highly accurate and interpretable ML model to stratify recurrence risk in ypN0 breast cancer patients after PORT. Functioning as a risk-stratification tool within this uniformly treated cohort, it may help identify patients at high risk of recurrence despite receiving PORT, who could be candidates for intensified surveillance or consideration of adjuvant therapy escalation. Its role in personalizing radiotherapy decisions requires prospective validation in studies including untreated control groups.