Establishment and Verification of a Prediction Model for Symptomatic Radiation Pneumonitis in Patients with Esophageal Cancer Receiving Radiotherapy

Background: This study aimed to determine the value of the significant index in predicting symptomatic radiation pneumonitis (RP) in esophageal cancer patients, establish a nomogram prediction model, and verify the model. Material/Methods: The patients enrolled were divided into 2 groups: a model group and a validation group. According to the logistic regression analysis, the independent predictors for symptomatic RP were obtained, and the nomogram prediction model was established according to these independent predictors. The consistency index (C-index) and calibration curve were used to evaluate the accuracy of the model, and the prediction ability of the model was verified in the validation group. Recursive partitioning analysis (RPA) was used for the risk stratification analysis. Results: The ratio of change regarding the pre-albumin at the end of treatment (P=0.001), platelet-to-lymphocyte ratio during treatment (P=0.027), and neutrophil-to-lymphocyte ratio at the end of treatment (P=0.001) were the independent predictors for symptomatic RP. The C-index of the nomogram model was 0.811. According to the risk stratification of RPA, the whole group was divided into 3 groups: a low-risk group, a medium-risk group, and a high-risk group. The incidence of symptomatic RP was 0%, 16.9%, and 57.6%, respectively. The receiver operating characteristic curve also revealed that the nomogram model has good accuracy in the validation group. Conclusions: The developed nomogram and corresponding risk classification system have superior prediction ability for symptomatic RP and can predict the occurrence of RP in the early stage.