Modern inverse planning radiotherapy techniques allow for organs at risk (OARs) to evade radiation doses that they would have been subjected to with earlier techniques. The extent to which patient outcomes may be improved using these techniques depends on the delineation accuracy of target volumes and OARs on medical images as well as clinical dose constraints applied to regions of interest (ROIs). The recent discovery of bilateral "tubarial" salivary glands, which were found in the nasopharynx using prostate-specific membrane antigen (PSMA) positron emission tomography (PET), raises concerns over how dose to this region might affect patient outcomes. The dose response of the major salivary glands is known to be variable, and it is possible that the dose in tubarial glands constitutes a missing variable in the optimization of head and neck (HN) radiotherapy plans. A necessary first step toward conducting clinical studies that include the tubarial glands in plan optimization is to develop methods for delineating these glands without the use of PSMA PET images, as their acquisition is not considered a part of the standard of care for HN patients. In this study, we develop an open-source program, Organogenesis, for the auto-segmentation of tubarial glands using only computed tomography (CT) images. A predictive model is trained using contours derived from PSMA PET images, allowing for accurate delineation of tubarial glands, which cannot be manually contoured using CT only. Organogenesis provides a predictive model for tubarial glands that can be iteratively improved on with additional data, creating a viable pathway to clinical studies that can assess the importance of incorporating tubarial glands into HN radiotherapy plan optimization.
Keywords: autosegmentation; optimization; psma; psma pet; radiotherapy; saliva glands; tubarial; xerostomia.
Copyright © 2022, Sample et al.