A simple-to-use clinical nomogram to predict radiation esophagitis among esophageal cancer patients receiving radiotherapy: a retrospective cohort study.

Background: Moderate-to-severe acute radiation esophagitis (MSARE) is a treatment-limiting toxicity of thoracic radiotherapy (RT) in esophageal cancer (EC). Early identification of high-risk patients is essential to individualize supportive care and preserve treatment continuity. Purpose: To develop and internally validate a parsimonious clinical nomogram for predicting MSARE, interpret it with SHapley Additive exPlanations (SHAP), and benchmark it against four machine learning (ML) algorithms. Methods: We retrospectively reviewed 151 EC patients who completed thoracic RT between January 2022 and December 2024. Candidate predictors were screened with LASSO regression and entered into a multivariable logistic model, visualized as a nomogram. Discrimination, calibration, clinical utility, and learning behavior were assessed by the area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis (DCA), and a learning curve. Logistic regression was benchmarked against XGBoost, LightGBM, AdaBoost, and K-Nearest Neighbors (KNN) under stratified 5-fold cross-validation. Results: MSARE occurred in 81 of 151 patients (53.6%). Three independent predictors were retained: upper esophageal tumor (OR 7.32), hypertension (OR 2.35), and serum albumin (OR 0.85; all P < 0.05). The nomogram achieved a 5-fold cross-validated training AUC of 0.832 and validation AUC of 0.816 (95% CI: 0.676-0.949), with good calibration ( Conclusions: An interpretable three-variable nomogram demonstrates promising internal discrimination for MSARE, yields comparable performance to four ML algorithms, and supports individualized supportive-care planning by clinicians.