Radio-morphology: Parametric shape-based features in radiotherapy
Abstract
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.
Conflicts of Interest
All authors have no potential conflict of interest to disclose, except the following three authors: Dr. McNutt reports grants from Radiation Oncology Institute, during the conduct of the study and grants from Canon Medical and Philips Healthcare, outside the submitted work. In addition, Dr. McNutt has a patent 20170259083 Method and Apparatus for Determining Treatment Region and Mitigating Radiation Toxicity, a patent 20170083682 Method, System, and Computer-Readable Media for Treatment Plan Risk Analysis issued, and a patent 20160378919 System and Method for Medical Data Analysis and Sharing issued. Dr. Taylor and Dr. McNutt have a patent US Patent 8,688,618 B2, T. R. McNutt, M. M. Kazhdan, and R. H. Taylor, “Shape Based Retrieval of Prior Patients for Automation and Quality Control of Radiation Therapy Treatment Plans”, Filed 23-Jun-2009, Issue date April 1, 2014, with royalties paid to Johns Hopkins University. The inventors receive a portion of the royalties. Mr. Bowers reports grants from Elekta, during the conduct of the study.
