A Promptable 3D-CT Foundation Model-Based Approach for Pulmonary Embolism

PURPOSE: Blood clot volume (BCV), defined as the total three-dimensional (3D) volume of the thrombus on computed tomography angiography (CTA), is an objective biomarker of pulmonary embolism (PE) severity whose clinical use is limited by time-consuming manual segmentation. This study evaluates ClotIA (Clot Interventional AI), a foundation model (FM)-based approach designed for rapid and interactive clot segmentation in PE. MATERIALS AND METHODS: RAPSv2, a foundation model derived from SAM2, was fine-tuned on a stratified sample of 309 patients from the RSPECT dataset (2020). Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and compared to that of nnUNet (no-new-Net). The predicted BCV was correlated with imaging biomarkers of PE severity. RESULTS: ClotIA achieved a mean DSC of 0.83 ± 0.06 after guided refinement, compared to 0.79 ± 0.10 at baseline (p < 0.001) and 0.81 ± 0.13 for nnUNet (p < 0.001). The predicted BCV showed strong agreement with the reference volume (r = 0.995; mean bias + 0.12 mL) and was significantly correlated with RV/LV diameter ratio (r = 0.62, p < 0.001) and RV/LV volume ratio (r = 0.68, p < 0.001). CONCLUSION: ClotIA enables rapid and reproducible 3D quantification of pulmonary embolism thrombi, correlating with established severity markers and providing the necessary basis for translating emerging biomarkers into clinical practice.