Predicting Radiation Esophagitis in Patients Undergoing Synchronous Boost Radiotherapy Post-Breast-Conserving Surgery

• Published in: Dose-response Vol. 23; no. 2; p. 15593258251335802
• Main Authors: Zhang, Huai-wen, Wang, Yi-ren, Li, Jingao, Huang, Wei, Xu, Bin, Pang, Hao-wen, Jiang, Chun-ling
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.04.2025; SAGE PUBLICATIONS, INC; SAGE Publishing
• Subjects: Nomograms; Original; Radiation therapy; radiotherapy; breast cancer; radiation esophagitis; machine learning; normal tissue complication
• ISSN: 1559-3258; 1559-3258
• DOI: 10.1177/15593258251335802

This study constructed a predictive model for occurrence of radiation esophagitis during breast-cancer radiotherapy. 308 breast-cancer patients were analyzed. Lasso regression identified crucial variables that were further integrated into a radiation esophagitis risk score, which was used to segregate patients into high- and low-risk groups. A nomogram model was designed for clinical applicability. Training and validations were performed to assess robustness and generalizability of proposed models, employing C-index, AUCs, calibration curves, and decision curves. SHAP algorithm was used for model interpretation, offering insights into the major contributory factors. Seven significant variables were identified by Lasso regression. C-indexes of nomograms of individual clinical variables and risk score were 0.795 and 0.784, respectively, exhibiting strong predictive ability. In internal validation, AUCs for risk score, nomogram, and logistic models were 0.784, 0.795, and 0.812, respectively. Calibration curves showed a close fit between predicted and observed outcomes across models. Decision curve analysis indicated logistic model’s superior clinical utility when the risk threshold was above 0.2. SHAP interpretation emphasized radiation dose, pruritus, molecular type, and hepatic dysfunction as top contributory factors for radiation esophagitis. Models based on interpretable machine learning offer an intuitive tool to assess risk of radiation esophagitis in breast-cancer radiotherapy. Graphical Abstract