An interpretable machine-learning model to predict moderate-to-severe acute radiation esophagitis following regional nodal irradiation for breast cancer

Abstract

Acute radiation esophagitis (ARE) is a clinically relevant toxicity occurring in patients with breast cancer receiving post-operative regional nodal irradiation. The present study aimed to develop and evaluate an interpretable machine-learning (ML) model for grade ≥2 ARE. A total of 324 patients were allocated by outcome-stratified random sampling (7:3) to a training set (n=228) and a held-out test set (n=96). A total of six consensus predictors were retained: Pre-existing pruritus, histological grade, body mass index (BMI), diabetes, tumor laterality and the relative esophageal volume receiving ≥30 Gy (RV_V30). In total, 10 ML algorithms were evaluated. Bootstrap internal validation (1,000 resamples) revealed that logistic regression (LR) had the lowest mean optimism among the three preferred candidates (0.027). Final model selection considered bootstrap stability and corrected performance together with held-out discrimination and calibration, model complexity and clinical interpretability. In the held-out test set, LR achieved an area under the curve value of 0.797 (95% confidence interval, 0.706–0.889), a Brier score of 0.179, a calibration slope of 1.258 and a calibration-in-the-large of −0.056. Decision curve analysis revealed positive net benefit relative to treat-all and treat-none strategies across threshold probabilities of 10–66%. SHapley Additive exPlanations identified histological grade, RV_V30 and BMI as the most influential predictors. The final model was implemented as a nomogram and web-based calculator. Overall, the present study demonstrates that this six-feature clinical-dosimetric model may support individualized risk stratification and radiotherapy planning, but prospective multicenter external validation is required before routine clinical use.