Development and interpretation of a dual-energy CT-based deep learning radiomics model for predicting new cerebral ischemic lesions after carotid artery stenting: a multicenter study.
Authors
Affiliations (7)
Affiliations (7)
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
- Department of Radiology, Lishui Central Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
- Department of Vascular Surgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
- Department of Radiology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- Department of Radiology, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China. [email protected].
- Department of Vascular Surgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China. [email protected].
Abstract
Early recognition of individuals at elevated risk for new ipsilateral ischemic lesions (NIILs) after carotid artery stenting (CAS) is vital for planning effective preventive interventions. The aim of this study was to develop a deep learning (DL) radiomics model to predict NIILs post-CAS from dual-energy CT (DECT) images. This study retrospectively enrolled patients from three centers. Carotid plaques were delineated on multiparametric DECT images. A combined model integrating clinical-radiological, handcrafted radiomics (HCR), and DL features was constructed using a support vector machine algorithm to predict NIILs. The model's performance was assessed through the area under the receiver operating characteristic curve (AUC). To improve the interpretability of the model, SHapley Additive exPlanations (SHAP) analysis was applied. This study involved 336 patients divided into the training (nā=ā135), internal validation (nā=ā58), and external test (nā=ā143) cohorts. NIILs were present in 38.5%, 37.9%, and 39.9% of the subjects, respectively. Symptomatic events and plaque ulceration were identified as independent risk factors for NIILs. The combined model incorporating 2 clinical-radiological risk factors, 9 HCR features, and 15 DL features demonstrated satisfactory performance in predicting NIILs, with AUCs of 0.908, 0.842, and 0.856 in the three cohorts, respectively. The predictions of the combined model were explained both locally and globally by SHAP analysis. The combined model demonstrated high accuracy in identifying patients at elevated risk for NIILs post-CAS and can serve as an interpretable tool for optimizing treatment strategies. Question Early prediction of new ipsilateral ischemic lesions (NIILs) after carotid artery stenting (CAS) is crucial for timely interventions, but no effective, interpretable predictive method exists. Findings The combined model incorporating deep learning radiomics features extracted from multiparametric dual-energy CT images and clinical-radiological features demonstrated high accuracy in predicting NIILs after CAS. Clinical relevance The combined model offers an interpretable tool for identifying patients at high risk for NIILs post-CAS, potentially improving personalized treatment strategies and patient outcomes by enabling targeted preventive care.