Voxel Based Analysis of Local Dose Patterns on Radiation Induced Xerostomia in Head and Neck Cancer with Machine Learning Methods

Abstract

Despite the technological advancements, xerostomia caused by the irradiation of parotid and/or submandibular glands significantly impacts a patient's quality of life and remains a clinical challenge. The mean dose within the salivary glands that neglects the local dose effect is an oversimplified predictor thus its efficacy is severely limited. This study aims to uncover the local dose patterns associated with the xerostomia by performing a voxel-based analysis of the spatial parotid dose. A cohort of 143 patients who underwent intensity modulated radiation therapy treatment were retrospectively selected for this study. A binary classification was adopted for predictive modeling: group 1 (102 patients) if grade 0 or 1 and group 2 (41 patients) if grade 2 or 3. The anatomy of the parotid gland from the computed tomography (CT) of these patients were standardized to that of a reference patient with rigid and multistage B-spline deformable registration. The resultant deformation field vectors were subsequently utilized to warp the parotid doses and dose gradients to the reference ones. To facilitate pattern recognition, the bilateral parotid doses and dose gradients were further regrouped into contra- and ipsi-lateral depending on their proximity to the tumor targets. Four supervised machine learning (ML) models including ReliefF, L1 regularized logistic regression (L1LR), ridge regression (RR), and random forest (RF) were employed to determine the feature importance of each voxel and discover the dose patterns within the glands that are correlated with xerostomia. The predictive performance of each model was evaluated with the area under the curves (AUC) of the receiver operating characteristic. The image registration accuracy of an average of 95.6% for the parotid gland standardization on CT images was achieved. The four ML models were assessed with 10-fold cross validation showing comparable performance and the AUC scores were 0.74 ± 0.09, 0.75 ± 0.08, 0.73 ± 0.10, and 0.77 ± 0.09 for ReliefF, L1LR, RR, and RF, respectively. The importance patterns showed that the doses to the superior and medial region of the contralateral parotid gland were more correlated with xerostomia from the ReliefF model but not others. Similar importance patterns were observed from the dose gradient distributions along the left-right and anterior-posterior directions. The voxel-based model, which takes into account the spatial information of dose and dose gradient within the parotid gland, demonstrated an improvement in xerostomia predictive modeling as compared to the mean dose predictor, particularly in the local dose effect on xerostomia. If these findings are validated with more patient data and independent studies, they could be incorporated into treatment planning of head and neck patients to reduce the toxicity occurrence.