# Development and validation of a predictive model for severe radiation-induced esophagitis in lung cancer patients undergoing moderate hypofractionated radiotherapy

## TL;DR

This study creates a model to predict severe esophagitis in lung cancer patients undergoing a specific type of radiotherapy, helping doctors identify high-risk patients and improve treatment strategies.

## Contribution

The novel contribution is the development of a predictive model using clinical and dosimetric factors to identify high-risk patients for severe radiation-induced esophagitis in moderate hypofractionated radiotherapy.

## Key findings

- The model identified five predictors of severe radiation-induced esophagitis using elastic-net and Firth-penalized logistic regression.
- The model showed moderate discrimination (AUC=0.608) and acceptable calibration for predicting high-risk patients.
- Decision curve analysis confirmed the model's clinical utility over 'treat-all' and 'treat-none' strategies.

## Abstract

Moderate hypofractionated radiotherapy (MHRT) is an important treatment modality for lung cancer, offering shorter courses and improved local control, yet it also markedly increases the risk of severe radiation-induced esophagitis (RIE; grade ≥3). Severe RIE compromises quality of life and adherence to therapy and may necessitate interruption of radiotherapy. This study aimed to develop a prediction model based on clinical and dosimetric factors to identify high-risk patients receiving MHRT and to facilitate individualized treatment strategies.

Lung cancer patients receiving moderate hypofractionated radiotherapy were included, with the endpoint defined as grade ≥3 radiation-induced esophagitis. Baseline characteristics were summarized using non-missing data only. During model development, in each outer bootstrap training set, candidate variables underwent single-rule imputation (median for continuous variables, mode for categorical variables) and standardization, followed by variable selection via elastic-net regression and model building with Firth-penalized logistic regression; the outcome itself was not imputed. Fully nested bootstrap validation (B=1000) was performed to assess internal robustness, with optimism-corrected performance metrics and 95% confidence intervals reported. Discrimination was evaluated using ROC curves and AUC, calibration by calibration plots and the Hosmer–Lemeshow test, and clinical utility through decision curve analysis (DCA). The optimal threshold was determined by the Youden index, with the corresponding confusion matrix presented. Finally, a nomogram was constructed to facilitate clinical visualization and application.

A total of 105 patients were included; the incidence of grade ≥3 RIE was 16.2% (17/105). Five predictors entered the final model via elastic-net selection: mean gross tumor volume (mean GTV), V5, D2cc, circumferential 2.6-Gy irradiated length, and circumferential 3.0-Gy irradiated length. The Firth-penalized logistic model showed good apparent performance: AUC=0.771, Brier score = 0.114, calibration slope = 1.16, and calibration intercept = 0.13. After optimism correction by fully nested bootstrap (B=1,000), discrimination decreased to AUC=0.608 (95% CI, 0.464–0.761) with a corresponding Brier score of 0.176 (95% CI, 0.114–0.247). The Hosmer–Lemeshow test yielded χ² = 7.84, p = 0.449, indicating acceptable overall fit. The Youden-index–derived optimal cutoff was 0.130, stratifying patients into high-risk (predicted probability ≥ 0.13) and lower-risk (< 0.13) groups. DCA demonstrated positive net benefit over “treat-all” and “treat-none” strategies across threshold probabilities of 0–0.8. Optimism-corrected calibration parameters were unstable, likely reflecting the limited number of events; these results should be interpreted with caution.

Using elastic-net feature selection and Firth logistic regression, we developed a model to predict severe (grade ≥3) RIE in lung cancer patients undergoing MHRT. The model exhibited moderate discriminatory ability with generally acceptable calibration, enables risk stratification and identification of high-risk patients, and is presented as a nomogram to support clinical application. It holds promise for guiding individualized radiotherapy decisions and the prevention of treatment-related complications.