Evaluation of Commercial Pelvic Autosegmentation Solutions in Anatomic Edge Cases of Patients Receiving Definitive Radiotherapy for Prostate Cancer

Abstract

To assess the performance of commercially available autosegmentation solutions of pelvic anatomy in edge cases of anatomic variation among patients receiving definitive radiotherapy (RT) for prostate cancer.In a single institution retrospective cohort of 830 patients with prostate cancer receiving definitive RT between 2011 and 2019, we identified 112 patients with anatomic variations seen on simulation CT imaging which may present challenging cases for precise delineation of anatomy, including reasons such as arthroplasty metal artifacts, extensive median lobe hypertrophy, "droopy" seminal vesicles, presence of urinary catheter, and history of transurethral resection of prostatic tissue. Three commercially available solutions for pelvic anatomy autosegmentation (AS) employing deep learning and model-based segmentation were applied to treatment planning CT scans to generate segmented volumes of the prostate, rectum, bladder and femoral heads in DICOM-RT format. To quantify the accuracy of software-generated contours, Dice similarity coefficients (DSC) and Hausdorff distances (HD) were calculated for each AS contour with a comparator manually segmented reference contour. Calculations were performed in R using the RadOnc package and manually segmented contours were approved by an expert radiation oncologist with 19 years of experience.A total of 112 patients were identified for inclusion after manual review of treatment planning CTs. Contours were generated using two atlas/model-based segmentation products and a deep learning segmentation method. The mean DSC for the prostate, bladder, rectum was calculated for each segmentation method (Table 1). Deep learning segmentation outperformed model-based methods for all structures with the highest mean DSC but still had significant disagreement with manually segmented structures. Hip arthroplasty in particular reduced overall performance more than other anatomical edge cases, followed by prostatic hypertrophy.Anatomic edge cases present a challenging and relevant consideration in clinical implementation of autosegmentation software.