AI-based delineation based on clinical data in MR-guided brachytherapy for cervical cancer reduces delineation time - A prospective paired trial.

BACKGROUND AND PURPOSE: In MR-guided brachytherapy, organs at risk (OAR) are manually delineated during the treatment planning process while the patient waits for the treatment to start with the applicator in place. This study evaluates the delineation time gain and bias introduction of an in-house created artificial intelligence (AI) model trained with a manually uncurated clinical dataset. MATERIALS AND METHODS: An nnU-Net was trained on clinical T2w-MR scans for brachytherapy. OAR and target structures underwent automatic interpolation but no manual recontouring. Following clinical implementation, delineation times and structure sets from 32 individual patients with 64 pulsed dose rate (PDR) treatments were collected prospectively from December 2023 to December 2024. Each patient received two treatment fractions: the first was based on AI-assisted delineations corrected by the oncologist, and the second was with the oncologist delineating from scratch without AI assistance. The reduction in delineation time and differences between the uncorrected AI-based and clinically approved delineations were analysed. RESULTS: AI-based delineations resulted in a time reduction of 10.1 min (95% CI: [7.4, 12] minutes), corresponding to a 31% decrease in delineation time. The AI-based delineations were generally rated acceptable by oncologists, requiring only minor corrections in most cases. When using AI support, the median of the mean surface distance between the AI and clinically used delineation for all OARs and targets was below 2.1 mm. CONCLUSION: AI-based delineation models, trained on clinical data without additional manual recontouring, can reduce delineation time. In the current clinical trial, the delineation time was reduced by 31%.