Interpretable Machine Learning for Predicting Symptomatic Pneumonitis in Locally Advanced Non-Small Cell Lung Cancer Patients Treated with Concurrent Chemoradiotherapy and Immune Checkpoint Inhibitor Consolidation.

Abstract

The rate of grade 2 and higher pneumonitis has increased with the use of immune checkpoint inhibitors (ICI) following chemoradiotherapy (CRT) for lung cancer, which may alter previously established dose-volume constraints (DVC). In this study, we used an interpretable machine learning model with clinical and dosimetric features to predict grade 2+ pneumonitis and determine DVC associated with pneumonitis for locally advanced non-small cell lung cancer (LA-NSCLC) radiotherapy (RT). Between October 2017 and December 2021, 223 consecutively treated patients with LANSCLC treated with CRT and ICI were retrospectively reviewed. The dataset was split into training and test sets (n = 144/79). Clinical features included age, sex, smoking status, pack-years, BMI, ECOG PS, COPD, tumor location, delivered dose, RT technique, chemotherapy agent and volume of GTVp/GTVn. A total of 228 dosimetric features from the heart, contralateral/ipsilateral lung and lungs-IGTV were extracted, including the minimum/mean dose to the hottest x% volume (Dx%[Gy]/MOHx%[Gy]; x was 5-95 in 5% increments) and minimum/mean/maximum dose and percent volume receiving at least xGy (VxGy [%]; x was 5-60 in 5Gy increments), as well as the overlapping volume of each structure with PTV and the distance from each structure to GTVp/GTVn. Feature selection was performed using Boruta, followed by collinearity removal based on the variance inflation factor. The explainable boosting machine (EBM) was trained on the selected features. The performance of EBM on the test set was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with that of blackbox (BB) models, including extreme gradient boosting (XGB), random forest (RF), and supporting vector machine (SVM). The global explanation of each feature's contribution to the predictions provided by the EBM was used to determine DVC. Shapley additive explanations (SHAP) were used to explain BB predictions. Selected features, ranked in order of EBM's overall feature importance, were V25Gy [%] and MOH65%[Gy] in the ipsilateral lung, the maximum dose in the heart, MOH30%[Gy] in the contralateral lung, and BMI. No dosimetric features in the lungs-IGTV were selected. The SHAP values of three BB models showed similar trends to the feature importance of the EBM. The global explanations of the EBM suggested that to mitigate the risk of pneumonitis, the ipsilateral lung should have V25Gy [%] < 36.8% and MOH65%[Gy] < 39.5Gy, and the heart should have D0.03cc [Gy] < 66.0Gy. Furthermore, an increased risk of pneumonitis was indicated with an increase in BMI, and, surprisingly, a decrease in MOH30%[Gy] in the contralateral lung. The EBM showed the best performance for predicting grade 2+ pneumonitis (AUC = 0.739), followed by RF, SVM, and XGB (AUC = 0.735, 0.733, and 0.717). EBM has the potential to predict grade 2+ pneumonitis in LA-NSCLC patients treated with CRT and ICI, while providing guidance on DVC.