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In this single institution retrospective study, we reviewed 950 consecutive patients with prostate adenocarcinoma receiving definitive radiotherapy between 2011 and 2019, and identified among them 112 patients with anatomic variations (edge cases) seen on simulation CT and/or MRI imaging. These variations included hip arthroplasty, prostate median lobe hypertrophy, so-called “droopy” seminal vesicles, presence of a urinary catheter, and others. A separate cohort of 19 “normal” cases were randomly selected for inclusion. Prostate, rectum, bladder, and bilateral femoral heads were manually segmented on all CT simulation images (where present) and were ultimately used clinically for radiation treatment planning.

We leveraged this imaging dataset to assess the comparative performance of deep learning, atlas-based, and model-based autosegmentation methods across both normal and edge case cohorts: <INSERT DOI/LINK HERE> https://doi.org/10.1016/j.phro.2023.100413.