Automated CT Pulmonary Angiography-Derived Periclot Radiomics for CTEPH Detection.

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Abstract

CTEPH remains underdiagnosed and can be difficult to differentiate on routine CTPA from acute PE and from patients without pulmonary thromboembolism. We developed and evaluated a fully automated CTPA-based machine learning algorithm combining vascular blood volume metrics with clot and periclot imaging features. In this retrospective multicenter study, intraparenchymal pulmonary vessels were segmented using a scale-space particle approach, deep learning models estimated vessel radius, clot probability, and clot area from cross-sectional patches along the vessel axis, and subject-level radiomic and blood volume features were used to train an XGBoost classifier on a Northwestern University (NU) cohort (89 CTEPH; 176 controls). The trained model was then applied (without retraining) to held-out NU data, an independent acute PE cohort (152 patients) as an exploratory generalization test, and an external cohort from Brigham and Women's Hospital (23 CTEPH; 34 controls). Among 474 participants (mean age, 51 years ± 16; 299 men), the model achieved an AUC of 0.87 (95% CI: 0.79, 0.94) for CTEPH versus controls in internal testing, 0.74 (95% CI: 0.61-0.86) in external testing, and 0.89 (95% CI 0.83-0.93) for CTEPH versus acute PE. Excluding periclot features reduced discrimination, particularly for CTEPH versus acute PE (AUC 0.71; 95% CI: 0.63, 0.79). These findings support the feasibility of automated CTPA-derived vascular, clot, and periclot features for differentiating CTEPH from acute PE and controls without evidence of pulmonary thromboembolism, with further validation needed to assess generalizability across institutions.

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