Dosimetric factors and radiomics features within different regions of interest in planning CT images for improving the prediction of radiation pneumonitis.

Abstract Purpose This study aimed to establish machine learning models using dosimetric factors and radiomics features within five regions of interest (ROIs) in treatment planning computed tomography (CT) images to improve the prediction of symptomatic radiation pneumonitis (RP) (grade ≥ 2). Methods and materials This study retrospectively collected 79 lung cancer patients (25 RP ≥ 2) who underwent chemoradiotherapy between 2015 and 2018. We defined five ROIs in planning CT images: gross tumor volume (GTV), planning tumor volume (PTV), PTV-GTV, total lung (TL)-GTV, and TL-PTV. We calculated the mean dose, V5, V10, V20, and V30 within TL-GTV and TL-PTV, and the mean dose within the other ROIs. A total of 1,924 radiomics features were extracted from all five ROIs. We selected the best predictors for classifying two groups of patients using a sequential backward elimination support vector machine model. A permutation test was used to assess its statistical significance (P Results The best predictors for symptomatic RP were the combination of 11 radiomics features, 5 dosimetric factors, age, and T stage, achieving an area under the curve (AUC) of 0.94 [95% confidence interval (CI): 0.85–1] [accuracy: 90%; sensitivity: 80% (95% CI: 44%–96%); specificity: 95% (95% CI: 73%–100%); P:8×10-4]. The clinical characteristics, dosimetric factors and their combination showed limited predictive power [accuracy: 63.3%, 70%, and 70%; AUC (95% CI): 0.73 (0.54–0.92), 0.53 (0.31–0.75), and 0.72 (0.51–0.92), respectively]. The radiomics features of PTV-GTV and TL-PTV outperformed those of the other ROIs [accuracy: 76.7% and 76.7%; AUC (95% CI): 0.82 (0.65–0.99) and 0.80 (0.59–1), respectively]. Conclusions Combining dosimetric factors and radiomics features within different ROIs can improve the prediction of symptomatic RP. Our results can help physicians adjust the radiation dose distribution of the dose-sensitive lungs and target volumes based on personalized RP estimates.