A Clinical-Radiomics Nomogram for Predicting Early Ischemic Stroke Risk in Patients with Transient Ischemic Attack

Abstract

To develop and validate a clinical-radiomics nomogram for predicting early ischemic stroke risk in patients who sustain a transient ischemic attack (TIA). A retrospective training dataset (n= 76) and a prospective validation dataset (n= 34) of patients with TIA were studied. Image processing was performed using ITK-snap and Artificial Intelligent Kit. Radiomics features were selected in R. A nomogram predicting recurrent TIA/stroke in 90 days as a recurrent ischemic event was established. Model performance was assessed by computing the receiver operating characteristic curve and decision curve analysis (DCA). We found a higher proportion of diabetes and hypertension in the patients with recurrent TIA compared with the stable patients in both the training and validation datasets (P < 0.05). Recurrent patients had significantly higher ABCD2 scores and plaque scores compared to stable patients. ABCD2 score and necrotic/lipid core area were independent risk factors for recurrent ischemic events (odds ratio [OR], 2.75; 95% confidence interval [CI], 1.47-6.40; and OR, 1.20; 95% CI, 1.07-1.41, respectively). The radiomics model had area under the curve values of 0.737 (95% CI, 0.715-0.878) in the training dataset and 0.899 (95% CI, 0.706-0.936) in the validation dataset, which was superior to the ABCD2 score and plaque model for predicting stroke recurrence (P < 0.05). The nomogram predicting recurrent ischemic events was 0.923 (95% CI, 0.895-0.978) in the training dataset and 0.935 (95% CI, 0.830-0.959) in the validation dataset. DCA confirmed the clinical value of this nomogram. The nomogram, based on clinical ABCD2 score, carotid plaque components and radiomics score, shows good performance in predicting the risk of recurrent ischemic events in patients with TIA.