2026-10-12T11:44:37Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33718531 2026-09-14T04:43:53Z category_16 portal_316 item_type_3 month_year_09_2026

Data Sheet 2_Development of a preoperative prediction model for vascular recanalization in cervical artery dissection based on HR-VWI radiomics features combined with clinical risk factors.csv Tao Li (86810) Yue Ma (384284) Lei Wang (6656) Meiling Zhang (352540) Xueliang Tian (6831986) Xuanxiao Zhang (21680120) Wenjing Gu (4978097) Hongwei Zhou (93199) Jiaxin Liu (833821) Physiology artificial intelligence CEAD cervical artery dissection high-resolution vessel wall imaging multimodal imaging prognostic prediction radiomics risk stratification Objective<p>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.</p>Methods<p>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).</p>Results<p>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.</p>Conclusion<p>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.</p> 2026-09-14T04:43:53Z Dataset Dataset 10.3389/fphys.2026.1886681.s002 https://figshare.com/articles/dataset/Data_Sheet_2_Development_of_a_preoperative_prediction_model_for_vascular_recanalization_in_cervical_artery_dissection_based_on_HR-VWI_radiomics_features_combined_with_clinical_risk_factors_csv/33718531 CC BY 4.0