Feasibility and time gain of implementing artificial intelligence-based delineation tools in daily magnetic resonance image-guided adaptive prostate cancer radiotherapy

Background and Purpose: Daily magnetic resonance image (MRI)-guided radiotherapy plan adaptation requires time-consuming manual contour edits of targets and organs at risk in the online workflow. Recent advances in auto-segmentation promise to deliver high-quality delineations within a short time frame. However, the actual time benefit in a clinical setting is unknown. The current study investigated the feasibility and time gain of implementing online artificial intelligence (AI)-based delineations at a 1.5 T MRI-Linac. Materials and methods: Fifteen consecutive prostate cancer patients, treated to 60 Gy in 20 fractions at a 1.5 T MRI-Linac, were included in the study. The first 5 patients (Group 1) were treated using the standard contouring workflow for all fractions. The last 10 patients (Group 2) were treated with the standard workflow for fractions 1 up to 3 (Group 2 – Standard) and an AI-based workflow for the remaining fractions (Group 2 – AI). AI delineations were delivered using an in-house developed AI inference service and an in-house trained nnU-Net. Results: The AI-based workflow reduced delineation time from 9.8 to 5.3 min. The variance in delineation time seemed to increase during the treatment course for Group 1, while the delineation time for the AI-based workflow was constant (Group 2 – AI). Fewer occurrences of readaptation due to target movement occurred with the AI-based workflow. Conclusion: Implementing an AI-based workflow at the 1.5 T MRI-Linac is feasible and reduces the delineation time. Lower variance in delineation duration supports a better ability to plan daily treatment schedules and avoids delays.