Frontiers in physiology · 2026
Development of a preoperative prediction model for vascular recanalization in cervical artery dissection based on HR-VWI radiomics features combined with clinical risk factors.
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Li Tao, Ma Yue, Wang Lei, Zhang Meiling, Tian Xueliang, Zhang Xuanxiao, Gu Wenjing, Zhou Hongwei, Liu Jiaxin
Аннотация
OBJECTIVE: To explore the feasibility of constructing a prediction model for vascular recanalization in cervical artery dissection (CeAD) based on high-resolution vessel wall imaging (HR-VWI) radiomics features combined with clinical risk factors.
METHODS: A total of 110 patients with CeAD were retrospectively enrolled. Clinical baseline data and HR-VWI images were collected. The Radiomics Intelligent Analysis Software (RIAS) was used for image preprocessing, ROI segmentation, and radiomics feature extraction and screening. Combined with clinical factors, multiple prediction models including random forest, gradient boosting decision tree, and logistic regression (LR) were constructed. Model performance was evaluated using AUC, calibration, Brier score, and decision curve analysis (DCA).
RESULTS: A total of 11 features (6 radiomics + 5 clinical) were selected through a multi-step workflow (ICC filtering → t-test → LASSO → backward AIC selection). The LR model achieved the best performance, with an AUC of 0.757 (95% CI: 0.531-0.983) and average precision of 0.95 in the independent test set. The model showed acceptable calibration (Brier score = 0.129; intercept = 0.913; slope = 0.896) and favorable clinical utility by DCA. Hyperuricemia, hyperlipidemia, smoking, headache, stenosis degree, and specific radiomics features were independent predictors of recanalization. Wavelet-HLH_glszm_Small Area High Gray Level Emphasis and wavelet-LHH_glcm_SumEntropy showed the most significant predictive effects. However, given the limited sample size and few events, these results should be interpreted as preliminary.
CONCLUSION: The combined LR model shows moderate discriminative ability for predicting vascular recanalization in CeAD. As a proof-of-concept study, it provides preliminary reference for prognostic assessment, while external validation in larger multi-center cohorts is warranted before clinical implementation.
METHODS: A total of 110 patients with CeAD were retrospectively enrolled. Clinical baseline data and HR-VWI images were collected. The Radiomics Intelligent Analysis Software (RIAS) was used for image preprocessing, ROI segmentation, and radiomics feature extraction and screening. Combined with clinical factors, multiple prediction models including random forest, gradient boosting decision tree, and logistic regression (LR) were constructed. Model performance was evaluated using AUC, calibration, Brier score, and decision curve analysis (DCA).
RESULTS: A total of 11 features (6 radiomics + 5 clinical) were selected through a multi-step workflow (ICC filtering → t-test → LASSO → backward AIC selection). The LR model achieved the best performance, with an AUC of 0.757 (95% CI: 0.531-0.983) and average precision of 0.95 in the independent test set. The model showed acceptable calibration (Brier score = 0.129; intercept = 0.913; slope = 0.896) and favorable clinical utility by DCA. Hyperuricemia, hyperlipidemia, smoking, headache, stenosis degree, and specific radiomics features were independent predictors of recanalization. Wavelet-HLH_glszm_Small Area High Gray Level Emphasis and wavelet-LHH_glcm_SumEntropy showed the most significant predictive effects. However, given the limited sample size and few events, these results should be interpreted as preliminary.
CONCLUSION: The combined LR model shows moderate discriminative ability for predicting vascular recanalization in CeAD. As a proof-of-concept study, it provides preliminary reference for prognostic assessment, while external validation in larger multi-center cohorts is warranted before clinical implementation.