Radiological Prediction Model of Lung Radiation Pneumonitis Based on Dose Line Segmentation

Abstract

Due to the differences in patients' lung conditions, radiation pneumonia (RP) sometimes occurs even if the lung's dose limitation is met. We are aiming to further predict the occurrence odd of radiation pneumonitis in NSCLC after receiving radiotherapy. Consider based on the dose-volume level to segment the lungs receiving radiation, use radiomic modeling, and evaluate its predictive effectiveness.The study retrospectively enrolled 306 CT scans from 102 NSCLC patients who received IMRT or VMAT from Oct 2015 to Aug 2016. A 0-5 grade RP occurred within 12 months after radiotherapy, considered as the endpoint event. The evaluation of radiation pneumonia in this study case was based on CTCAE 5.0. The whole cohort was divided into two groups, the RP and non-RP. The lung was segmented based on the dose-volume line as V5-10, V10-20, V20-30, V30-40, V40-50, and V50-60 as the region of interest (ROI). Then we register the ROI to the lung of the CT images in the middle and after the radiotherapy. We utilize open source software to extract the image features for each ROI to develop and validate the radiomics model to predict radiation pneumonitis. Two classifier models of random forest (Random forest, RF) and support vector machine (SVM) were used for classifying and learning samples' characteristics to distinguish between the pneumonia-occurring group and the non-occurring group effectively. We randomly divided the sample size into a training and validation group at a 4:1 ratio, and the five-fold cross-validation model (K fold cross-validation) was used for verification. The predictive performance was evaluated using overall accuracy for this triple classification task.The overall incidence of radiation pneumonia was 36%, and the incidence of severe radiation pneumonia (grade 3 and above) was 4%. The support vector machine (SVM) model has the best prediction performance compared to random forest, with an average accuracy rate of 0.72 and an average AUC value of 0.66. Under different dose line volume segmentation, the accuracy of the prediction models in pre-, mid-, and after treatment are 0.724, 0.744, 0.775 (V5-10), 0.736, 0.719, 0.737, (V10-20), 0.728, 0.696, 0.693 (V20-30), 0.671, 0.678, 0.684 (V30-40), 0.747, 0.724, 0.705 (v40-50), 0.799, 0.743, 0.726 (V50-60), respectively. The model constructed in the 50-60Gy area of CT before radiotherapy has the best performance in predicting radiation pneumonia.Radiation pneumonia is a side effect that needs to be paid attention to in thoracic radiotherapy, and its incidence and severity directly affect patients' survival. Traditional radiation pneumonia prediction methods are relatively general. With the development of precise radiotherapy, further accurate radiation pneumonia prediction models are required. Radiomics can become a reliable prediction method for radiation pneumonia.