Predicting xerostomia in head and neck cancer using imaging biomarkers from daily tomotherapy MVCTs

Abstract

Purpose or Objective There is a growing interest in predicting late xerostomia in head and neck (H&N) cancer patients using image biomarkers (IMBs) calculated on CT images acquired at sparse intervals over the course of radiotherapy. In delivering radiotherapy with image guidance (IG) the TomoTherapy (Accuray, Sunnyvale, CA, USA) system allows daily IG mega-voltage CT (MVCT) scans to be acquired with ease. The aim of this study was to 1) investigate whether changes in daily MVCT IMBs calculated on the parotid glands could predict late xerostomia (grade>1) and 2) establish the predictive power of the IMBs at different intervals of the radiotherapy course. Material and Methods As illustrated in Figure 1, IMBs (N=73) consisting of first and higher order features were calculated by textural analysis on the parotid glands of daily MVCT images (0.76x0.76x6mm) of 60 H&N cancer patients. All patients were treated in 30 fractions (fx) with CTCAE toxicity recorded at 12 months. Linear regression was calculated on the IMBs over 6 different time intervals of treatment (first 5, 10, 15, 20, 25 and 30 fx) and the slope of the regression extracted. To select the best (<9) IMBs for prediction on each interval, LASSO analysis was used with 4-fold cross validation (100 times). 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 30 combinations with the highest median mAUC were selected for each interval and their median mAUC distributions compared using a t-test. Results Of the 60 patients, 27% reported xerostomia (grade>1) at 12 months. The highest median mAUCtrain / mAUCtest was 0.93/0.80, 0.90/0.75, 0.89/0.73, 0.93/0.82, 0.90/0.75 and 0.94/0.84 for the slopes of the first 5, 10, 15, 20, 25, 30 fractions, respectively. As shown in Figure 2, the best performing 30 combinations of predictors for the first 5, 20 and 30 fractions performed better in median mAUCtrain and mAUCtest compared to the others. However, compared to the first 5 fractions the AUC distributions for 30 and 20 fx were not significantly higher (p>0.05). Conclusion Conclusion These preliminary results indicate that it may be possible to predict late xerostomia in H&N cancer patients using IMBs calculated on daily MVCT images. It is interesting to note that the highest AUCs, which may indicate the strongest predictors of late xerostomia, were present after the first 5 fractions. These findings should be confirmed on a larger cohort but open up the prospect of setting adaptation protocols in H&N cancer after just one week.