In this study, we analyzed baseline CT- and MRI-based image features of salivary glands to predict radiation-induced xerostomia after head-and-neck cancer (HNC) radiotherapy. A retrospective analysis was performed on 216 HNC patients who were treated using radiotherapy at a single institution between 2009 and 2016. CT and T1 post-contrast MR images along with NCI-CTCAE xerostomia grade (3-month follow-up) were prospectively collected at our institution. Image features were extracted for ipsilateral/contralateral parotid and submandibular glands relative to the location of the primary tumor. Dose-volume-histogram (DVH) parameters were also acquired. Features that were correlated with xerostomia (p<0.05) were further reduced using a LASSO logistic regression. Generalized Linear Model (GLM) and the Support Vector Machine (SVM) classifiers were used to predict xerostomia under five conditions (DVH-only, CT-only, MR-only, CT+MR, and DVH+CT+MR) using a ten-fold cross validation. The prediction performance was determined using the area under the receiver operator characteristic curve (ROC-AUC). DeLong’s test was used to determine the difference between the ROC curves. Among extracted features, 13 CT, 6 MR, and 4 DVH features were selected. The ROC-AUC values for GLM/SVM classifiers with DVH, CT, MR, CT+MR and all features were 0.72±0.01/0.72±0.01, 0.73±0.01/0.68±0.01, 0.68±0.01/0.63±0.01, 0.74±0.01/0.75±0.01, and 0.78±0.01/0.79±0.01, respectively. DeLong’s test demonstrated an improved in AUC for both classifiers with the addition of all features compared to DVH, CT, and MR-alone (p<0.05) and the SVM CT+MR model (p=0.03). The integration of baseline image features into prediction models has the potential to improve xerostomia risk stratification with the ultimate goal of personalized HNC radiotherapy.
Radiomics is a promising approach to identify patients at high risk of having pulmonary dysfunction caused by radiotherapy. This study aims to identify optimal radiomic input features for predicting pulmonary function. Forced expiratory volume in first second (FEV1) and forced vital capacity (FVC) were measured for 257 patients between 3 months prior to and 1 week after the first radiotherapy. FEV1/FVC ratio dichotomized at 70% was used as a target variable. Each patient had a radiotherapy planning CT and associated contours of gross tumor volume and left/right lungs. A total of 2,658 radiomic features were extracted and categorized into five levels: shape (S), first- (L1), second- (L2) and higher-order (L3) local texture, and global texture (G) features, as well as four multilevel groups: S+L1, S+L1+L2, S+L1+L2+L3, and S+L1+L2+L3+G. Nested cross-validation (NCV) was used to identify optimal input features. Cross-validated glmnet models optimized with unilevel or multilevel features were used to assess predictive performance on outer CV test sets. In unilevel analysis, the highest test AUC of 0.743±0.067 was obtained from NCV models optimized with L1 features. The best performance was achieved from NCV models optimized with S+L1+L2 features with AUC of 0.752±0.063. Paired Wilcoxon signed rank test results showed that AUC values of NCV models optimized with S, L2, L3, G or S+L1+L2+L3 features were statistically significantly different from those optimized with S+L1+L2 features (P<0.05). The multilevel analysis strategy will help to handle and optimize radiomic input features.