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

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 < 0.05). Calibration curves demonstrated high concordance between predicted and actual outcomes, while decision curves indicated superior clinical net benefit. Conclusion: The multimodal radiomics-clinical big data model accurately predicts interventional efficacy in carotid stenosis, providing reliable evidence for personalized treatment decisions.