Machine Learning Prediction of Carotid Intraplaque Hemorrhage: Fusing CT Radiomics and Clinical Data

Objective Develop and validate a machine learning model integrating CT-based radiomics with clinical and imaging data to diagnose carotid intraplaque hemorrhage (IPH), aiding clinical decision-making. Method This retrospective study analyzed 127 carotid plaques from 66 patients undergoing concurrent head/neck CTA and MRI at Ordos Central Hospital (April 2023–September 2024). Based on MRI results, plaques were categorized as IPH-positive (n = 41) or IPH-negative (n = 86). Radiomics features derived from CTA images were combined with clinical baseline characteristics. Three machine learning models were developed: 1) clinical baseline, 2) radiomics, and 3) integrated. Model performance was evaluated using AUC-ROC, calibration curves, and DCA. Results The logistic regression (LR) algorithm demonstrated superior diagnostic performance in both radiomics and integrated models, achieving test set AUCs of 0.804 (95% CI: 0.651–0.958) and 0.824 (95% CI: 0.682–0.965), respectively. The integrated model exhibited enhanced calibration and clinical utility via decision curve analysis. Plaque surface morphology was identified as a significant independent predictor of IPH. Conclusion The machine learning model combining CT-based radiomics features with clinical and imaging characteristics effectively diagnoses carotid artery IPH. This integrated approach provides valuable support for risk stratification and clinical decision-making in carotid plaque management, demonstrating significant potential for clinical application.