A Radiomics-Based Machine Learning Model for Predicting Pneumonitis During Durvalumab Treatment in Locally Advanced NSCLC

Takeshi Masuda, Daisuke Kawahara, Wakako Daido, Nobuki Imano, Naoko Matsumoto, Kosuke Hamai, Yasuo Iwamoto, Yusuke Takayama, Sayaka Ueno, Masahiko Sumii, Hiroyasu Shoda, Nobuhisa Ishikawa, Masahiro Yamasaki, Yoshifumi Nishimura, Shigeo Kawase, Naoki Shiota, Yoshikazu Awaya, Soichi Kitaguchi, Yuji Murakami, Yasushi Nagata, Noboru Hattori

Introduction: Pneumonitis represents one of the clinically significant adverse events observed in patients with non-small-cell lung cancer (NSCLC) who receive durvalumab as consolidation therapy after chemoradiotherapy (CRT). Although clinical factors such as radiation dose (e.g., V20) and interstitial lung abnormalities (ILAs) have been reported as risk predictors, accurate and objective prognostication remains difficult. This study aimed to develop a radiomics-based machine learning model to predict grade ≥ 2 pneumonitis. Methods: This retrospective study included patients with unresectable NSCLC who received CRT followed by durvalumab. Radiomic features, including first-order and texture and shape-based features with wavelet transformation were extracted from whole-lung regions on pre-durvalumab computed tomography (CT) images. Machine learning models, support vector machines, k-nearest neighbor, neural networks, and naïve Bayes classifiers were developed and evaluated using a testing cohort. Model performance was assessed using five-fold cross-validation. Conventional predictors, including V20 and ILAs, were also assessed using logistic regression and receiver operating characteristic analysis. Results: Among 123 patients, 44 (35.8%) developed grade ≥ 2 pneumonitis. The best-performing model, a support vector machine, achieved an AUC of 0.88 and accuracy of 0.81, the conventional model showed lower performance with an AUC of 0.71 and accuracy of 0.64. Conclusions: Radiomics-based machine learning demonstrated superior performance over clinical parameters in predicting pneumonitis. This approach may enable individualized risk stratification and support early intervention in patients with NSCLC.