Performance of an Artificial Intelligence Foundation Model for Prostate Radiotherapy Segmentation

Abstract Importance Artificial intelligence (AI) foundation models such as Segment Anything Model 2 (SAM 2) offer potential for semi-automated image segmentation with minimal fine-tuning, but their performance in specialized clinical tasks like radiation therapy planning are not well characterized. Objective To evaluate the performance of SAM 2 in segmenting pre-operative intact prostate and post-operative prostate fossa targets for prostate radiotherapy planning. Design, Setting, Participants Retrospective cohort study deploying and testing a foundation model for AI segmentation for prostate radiotherapy planning. CT simulation images and radiation plans were obtained from a single academic institution for patients undergoing prostate cancer treatment. Data analysis was performed from September 2024 to February 2025. Exposures AI segmentation with varying levels of human intervention, ranging from intervals of every 2nd to every 10th ground truth slice provided as input. Main Outcome and Measures Segmentation accuracy measured by Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) for intact and post-operative prostate target delineation. Results While SAM 2 outperformed interpolation in DSC and HD for both intact and post-operative prostate cancer patient cases, the AI segmentation accuracy was significantly better in the intact pre-operative patient cases where anatomic boundaries were better defined than post-operative patient cases. This is especially evident when sparse ground truth was provided simulating lower levels of human intervention. Conclusions and Relevance AI foundation models show promising application for specialized medical tasks such as prostate cancer radiotherapy segmentation with limited need for fine-tuning or retraining, although their clinical application will require further understanding of task-specific performance.