Evaluation of AI-based segmentation and synthetic CT generation for MRI-only prostate radiotherapy planning

There is an increasing use of magnetic resonance imaging (MRI) for segmentation of target volumes and organs at risk due to superior soft tissue contrast compared to computed tomography (CT). Conventional workflows require CT-MRI registration, introducing geometric uncertainty that can propagate to dose delivery. MRI-only radiotherapy, based on AI-generated synthetic CT (sCT) and segmentations from T2-weighted images, offers a single-modality approach, potentially eliminating registration errors.

Our study aims to evaluate whether an MRI-only workflow, using commercial AI models for segmentation and sCT generation (MVision), demonstrates performance comparable to expert-defined segmentations and reference CT-based calculations within the studied dataset.

We analyzed five segmentations on 19 male pelvis patients from the Gold Atlas dataset, providing deformably co-registered CT and MRI, sCTs, with multi-observer and consensus segmentations. We investigated segmentation accuracy, image similarity, and evaluated the dosimetric impact by comparing AI- to manual segmentations and CTs to sCTs. Wilcoxon signed-rank tests were used for statistical significance and leave-one-out kappa determined segmentation outliers.

Results show that AI-segmentations achieved accuracy comparable to interobserver variability across all organs. Median Dice coefficients exceeded 0.9 for the prostate and bladder, and distance metrics closely matched the manual results. The leave-one-out kappa showed that AI-segmentations were indistinguishable from manual observers for all organs. sCTs showed high similarity to the reference CTs, with a median mean absolute error of 34.0 HU and median structural similarity index of 0.93. Dose recalculations showed excellent agreement; median 3D-gamma pass rate was 99.8% (2%/2 mm), and mean absolute dose difference was 0.036 Gy. All evaluated clinical dose constraints remained fulfilled after recalculation on the sCT.

Our results confirm that an AI-segmentation and MRI-sCT workflow provides geometric, image, and dose accuracy comparable to manual- and CT-based references, supporting their integration into clinical workflows for pelvic radiotherapy.