Subregional analysis of the parotid glands: predicting late xerostomia in head and neck cancer

Abstract

Several studies have investigated the relationship between the clinical symptoms of xerostomia and the radiotherapy dose received by subregions of the parotid glands (e.g. superficial lobe/stem cell region). The purpose of this study was to compare the predictive power of image biomarkers (IMB) calculated on the superficial lobe (SL), deep lobe (DL) and whole parotid (WP) gland for identifying late xerostomia in head and neck (H&N) cancer patients. Material and Methods All patients (N=60) received 30 fractions (fx) on a TomoTherapy HiArt System (Accuray, Sunnyvale, CA, USA) with daily mega-voltage CT (MVCT) images with CTCAE toxicity recorded at 12 months. As illustrated in Figure 1, the MVCT images (0.76 x 0.76 x 6 mm) were used to calculate by textural analysis, seventy-three IMBs consisting of first and higher order features on the SL, DL and WP gland. For each feature, their values for the period 1-20fx were used to perform linear regressions and their slopes considered as potential predictors. LASSO analysis was used (100 times) with 4-fold cross validation to select the best predictors (<9) for each region. To evaluate the robustness of each of the 100 combinations, the patients were split into four folds. The training set made up of 3 folds and the testing set, the remaining fold, were used to test the predictive power of each of the LASSO combinations using a logistic regression model. This was evaluated by the Area Under the Curve (AUC), resulting in 4 AUCtest and 4 AUCtrain values which were averaged (mAUCtest and mAUCtrain). For robustness, this process was repeated 100 times for each combination of predictors and the median mAUCtest and mAUCtrain calculated. The 50 combinations of predictors with the highest median mAUC were selected for the SL, DL and WP and their median mAUCtest distributions compared using a t-test. Results Of the 60 patients, 27% reported xerostomia (grade>1) at 12 months. The highest median mAUCtrain / mAUCtest were 0.93/0.83, 0.90/0.79 and 0.93/0.80 for the SL, DL and WP, respectively. As shown in Figure 2, the 50 combinations of predictors that were selected for the WP gland yielded a significantly higher median mAUCtest compared to the DL (p<0.01). The selected combinations of the SL yielded higher median mAUCtest compared to the WP (p<0.01). Conclusion The slopes of the IMBs extracted from the DL (from fraction 1-20) have a lower predictive power compared to those extracted from the WP gland. Despite the smaller number of voxels considered when analyzing the SL, the IMBs extracted from the SL have a higher predictive power compared to those from the WP. However, this requires further validation on a larger cohort and different selected periods. To obtain a greater understanding of the differences in predictive power of each of these subregions requires further information on the underlying biological processes.