Predictive Modeling of Radiation Pneumonitis Induced by Chemoradiotherapy for Locally Advanced Non-Small Cell Lung Cancer Using Radiomics and Clinical Features

The purpose of this study is to establish a prediction model for the development of grade 2 or higher radiation pneumonitis (RP) using radiomics analysis of pretreatment CT images, PET images, and dose distribution, in addition to clinical factors, in patients with locally advanced non-small cell lung cancer (NSCLC) treated with radical chemoradiotherapy.We retrospectively evaluated 128 cases of locally advanced NSCLC treated with radical radiotherapy at our institution from 2008 to 2021. Clinical factors included age, sex, performance status (PS), KL-6, smoking history, histological type, clinical stage, and total radiation dose. Radiomics analysis was performed by analyzing treatment planning CT images, PET images, and dose distribution, and Rad-score (Radiomics-score) was calculated for the extracted features using Lasso-Cox regression. Rad-score (Radiomics-score) was calculated by Lasso-Cox regression for the extracted features. Risk factors were selected by univariate/multivariate analysis of clinical factors and Rad-score. Three models for predicting RP were developed from the identified risk factors using Nomogram: Clinical, Rad-score, and Combined model. The model was evaluated using area under the curve (AUC) based on receiver operating characteristic (ROC) curves and concordance index (C-index). RP was evaluated using the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. The cumulative incidence of Grade 2 or higher RP was evaluated using the Kaplan-Meier method.Of the 128 cases, grade 2 or higher RP was observed in 50 cases (39%). Regarding clinical factors, gender, smoking status, and histology were selected as significant predictors of RP. Lasso-Cox analysis of radiomics features selected 11 features from CT images, 7 features from PET images, and 16 features from dose distribution as predictors of RP, yielding a total of 34 factors. The combined model (C-index: 0.96, AUC: 0.92) showed the best discrimination performance compared to the clinical model (C-index: 0.73, AUC: 0.56) and the Rad-score model (C-index: 0.87, AUC: 0.92). Risk classification using the combined model showed that the 1-year cumulative incidence of grade 2 or higher RP was 65% in the high-risk group, significantly higher than 15% in the low-risk group (p<0.001).The combined model with Rad-score and clinical factors can predict grade 2 or higher RP in NSCLC patients with high accuracy.