Dose/Volume histogram patterns in Salivary Gland subvolumes influence xerostomia injury and recovery.

Xerostomia is a common consequence of radiotherapy in head and neck cancer. The objective was to compare the regional radiation dose distribution in patients that developed xerostomia within 6 months of radiotherapy and those recovered from xerostomia within 18 months post-radiotherapy. We developed a feature generation pipeline to extract dose volume histogram features from geometrically defined ipsilateral/contralateral parotid glands, submandibular glands, and oral cavity surrogates for each patient. Permutation tests with multiple comparisons were performed to assess the dose difference between injury vs. non-injury and recovery vs. non-recovery. Ridge logistic regression models were applied to predict injury and recovery using clinical features along with dose features (D10-D90) of the subvolumes extracted from oral cavity and salivary gland contours + 3 mm peripheral shell. Model performances were assessed by the area under the receiver operating characteristic curve (AUC) using nested cross-validation. We found that different regional dose/volume metrics patterns exist for injury vs. recovery. Compared to injury, recovery has increased importance to the subvolumes receiving lower dose. Within the subvolumes, injury tends to have increased importance towards D10 from D90. This suggests that different threshold for xerostomia injury and recovery. Injury is induced by the subvolumes receiving higher dose, and the ability to recover can be preserved by further reducing the dose to subvolumes receiving lower dose.

Radio-morphology: Parametric shape-based features in radiotherapy.

PURPOSE: In radiotherapy, it is necessary to characterize dose over the patient anatomy to target areas and organs at risk. Current tools provide methods to describe dose in terms of percentage of volume and magnitude of dose, but are limited by assumptions of anatomical homogeneity within a region of interest (ROI) and provide a non-spatially aware description of dose. A practice termed radio-morphology is proposed as a method to apply anatomical knowledge to parametrically derive new shapes and substructures from a normalized set of anatomy, ensuring consistently identifiable spatially aware features of the dose across a patient set. METHODS: Radio-morphologic (RM) features are derived from a three-step procedure: anatomy normalization, shape transformation, and dose calculation. Predefined ROI's are mapped to a common anatomy, a series of geometric transformations are applied to create new structures, and dose is overlaid to the new images to extract dosimetric features; this feature computation pipeline characterizes patient treatment with greater anatomic specificity than current methods. RESULTS: Examples of applications of this framework to derive structures include concentric shells based around expansions and contractions of the parotid glands, separation of the esophagus into slices along the z-axis, and creating radial sectors to approximate neurovascular bundles surrounding the prostate. Compared to organ-level dose-volume histograms (DVHs), using derived RM structures permits a greater level of control over the shapes and anatomical regions that are studied and ensures that all new structures are consistently identified. Using machine learning methods, these derived dose features can help uncover dose dependencies of inter- and intra-organ regions. Voxel-based and shape-based analysis of the parotid and submandibular glands identified regions that were predictive of the development of high-grade xerostomia (CTCAE grade 2 or greater) at 3-6 months post treatment. CONCLUSIONS: Radio-morphology is a valuable data mining tool that approaches radiotherapy data in a new way, improving the study of radiotherapy to potentially improve prognostic and predictive accuracy. Further applications of this methodology include the use of parametrically derived sub-volumes to drive radiotherapy treatment planning.