Prospective blinded clinical evaluation of a fully automated workflow for prostate radiotherapy

Background and purposeFully automated workflows integrating deep learning-based segmentation and treatment planning have shown promising results in research settings but require prospective evaluation under routine clinical conditions.This study assessed the clinical usability of a fully automated workflow for prostate radiotherapy compared with the conventional clinical workflow. Materials and MethodsTwenty-two consecutively treated patients undergoing prostate radiotherapy were included.For each patient, treatment planning was first performed according to the conventional workflow, followed by execution of a fully automated workflow combining deep learning-based segmentation and planning models within a commercial treatment planning system.Plans and structure sets were evaluated in a blinded, standardized manner by board-certified radiation oncologists and rated as "good", "acceptable", or "unacceptable".The preferred plan was selected for treatment.Processing times were recorded. ResultsClinically acceptable results were achieved in 86% of fully automated cases compared with 95% in the conventional workflow.Conventional plans were rated more frequently as "good" (17/22 vs 12/22), mainly due to differences in target delineation.Fully automated clinical target volumes were consistently smaller (median 54.2 cm vs 68.3 cm; p < 0.001).The fully automated workflow required a median of 9 minutes (range 8-10) compared with 87 minutes (range 70-130) for the conventional workflow. ConclusionsA fully automated workflow for prostate radiotherapy substantially reduces workload and processing time while achieving a high rate of clinical acceptability.Implementation as a draft-first approach with mandatory physician review appears feasible, although refinement of automated target delineation is warranted.