Deep Learning-Based Whole-Body Lesion Segmentation and Automated OMIS Computation on [68Ga]Ga-DOTA-TOC PET/CT: Technical Feasibility of a SwinUNETR Pipeline for Pre-PRRT Bone Marrow Involvement Scoring
This study demonstrates the technical feasibility of a SwinUNETR-based deep learning pipeline for automated whole-body lesion segmentation and Osteo-Medullary Invasion Score (OMIS) computation on [68Ga]Ga-DOTA-TOC PET/CT, achieving performance comparable to expert inter-observer agreement and offering a reproducible tool for pre-PRRT risk stratification in neuroendocrine tumor patients.