Combining Dosimetric and Radiomics Features for the Prediction of Radiation Pneumonitis in Locally Advanced Non-Small Cell Lung Cancer by Machine Learning

This study aimed to analyze the dosimetric factors and radiomics features of tumor and lungs in locally advanced non-small cell lung cancer (LANSCLC) to establish machine learning models and improve the prediction of grade (G) 2 radiation pneumonitis (RP).This study retrospectively collected data of 284 LANSCLC patients underwent concurrent chemoradiotherapy (CCRT) to a median dose of 64 Gy in 20-33 fractions between 2013 and 2021. Of this cohort, 21.1% of patients had ≥ G2 RP. There were 4 regions of interest (ROIs) had been identified in planning computed tomography images: gross tumor volume (GTV), ipsilesional lung (IL), contralesional lung (CL), and total lung (TL). We calculated the dose-volume histogram (DVH) from the lowest dose to the maximum dose increasing by degrees with 1 Gy, and extracted a total of 172 radiomics features from all the 4 ROIs. We selected the best predictors for classifying 2 groups of patients using a sequential backward elimination support vector machine model.The best predictors for ≥ G2 RP were the combination of 8 radiomics features and 7 dosimetric factors in training group, and the validation group achieved an area under the curve (AUC) of 0.847 (accuracy, 80.38%; sensitivity, 78.95%; specificity, 81.82%). The eight radiomic features included 2 from GTV while 1, 2 and 3 from IL, CL and TL, respectively. For dosimetric factors, V65 of GTV, V20, V50 and V55 of IL, V10 of CL, V20 and V55 of TL appeared to be significantly related to symptomatic RP. These dosimetric factors should be constrained to less than 99.2%, 50.0%, 17.5%, 13.0%, 39.5%, 32.0%, and 6.6%, respectively.Combining dosimetric factors and radiomics features within GTV, IL, CL and TL can improve the prediction of symptomatic RP in LANSCLC patients treated with CCRT. The results suggested the importance of V65 of GTV, V20, V50 and V55 of IL, V10 of CL, V20 and V55 of TL as predictors of symptomatic RP and provide useful information for optimization of treatment planning in the era of combination of radiotherapy and immunotherapy.