A Double-Blind Study to Evaluate the Feasibility of Using AI-Powered Auto-Segmentation in Prostate Cancer Treatment.

PURPOSE/OBJECTIVE(S) To evaluate whether the contours generated by an AI-powered auto-segmentation software can be used directly for prostate cancer radiation therapy. MATERIALS/METHODS A total of 23 prostate cases treated between 2018 and 2020 were retrospectively randomly selected. The prostate and surrounding organs at risk (OARs) were delineated on computed tomography (CT) scans by a radiation oncologist (RO) as ground-truth (GT) contour and by Artificial Intelligence (AI) model as AI contour. The GT and AI contour sets were anonymized randomly to be evaluated by a different RO. To eliminate the planner's bias, RapidPlans were used to generate treatment plans using AI and GT contours. The OAR dose on both plans were evaluated on GT contour sets to determine whether the AI contour can produce similar results as the GT contour. RESULTS In subjective double-blind test, AI contours show that 95.7% (22 out of 23) are scored as either "Great" (34.8%) or "Acceptable without changes" (60.9%), and only one case (4.3%) is evaluated as "Acceptable with minor changes". Note that the GT contours also have one case in the "minor change" category. Totally 69.6% (16 of 23) AI contours were considered equal or better than the GT counterparts. Paired t-tests showed no significant differences in dosimetric plan quality yielded by the AI and GT for the prostate, rectum, femoral heads, and penile bulb (P > 0.05). Statistically significant difference (P < 0.05) was observed on Bladder and Seminal Vesicles. However, no plan fails the RTOG-0815 organ tolerances. CONCLUSION The investigated auto-segmentation model for prostate anatomy provides compatible performance to manually delineated contours by a Radiation Oncologist.