TITLE:
Multimodal Radiomics Combined with Multidimensional Clinical Data for Predicting 1-Year Outcomes after Carotid Artery Stenting in Patients with Carotid Stenosis: A Single-Center Study
AUTHORS:
Fang Ouyang, Junfeng Su, Kai He
KEYWORDS:
Carotid Stenosis, Radiomics, Multimodal Imaging, Clinical Big Data, Efficacy Prediction Model, Carotid Stenting
JOURNAL NAME:
Journal of Biosciences and Medicines,
Vol.14 No.2,
February
9,
2026
ABSTRACT: Objective: To explore the feasibility of constructing a predictive model for the efficacy of interventional treatment (carotid stenting) for carotid stenosis based on multimodal imaging (CTA, MRI, ultrasound) omics features combined with clinical big data. Methods: A retrospective cohort of 312 patients undergoing carotid stenting was enrolled and divided into a training set (218 cases, 7:3 ratio) and a validation set (94 cases). A total of 1,428 radiomics features and clinical data were extracted. After dimensionality reduction via LASSO regression, a combined model was constructed using multivariable logistic regression and compared with single-modality imaging and clinical-only models. Results: The combined model incorporated 18 radiomics features and 5 clinical features. The validation set AUC reached 0.912, significantly higher than both the single-modality imaging model (0.829 - 0.865) and the clinical-only model (0.801) (all P