Radiomics Nomogram for Predicting Stroke Recurrence in Symptomatic Intracranial Atherosclerotic Stenosis - Tang, Min; Gao, Jie; Ma, Niane; Yan, Xuejiao; Zhang, Xin; Hu, Jun; Zhuo, Zhizheng; Shi, Xiaorui; Li, Ling; Lei, Xiaoyan; Zhang, Xiaoling (Frontiers in Neuroscience, 2022)
Objective: To develop and validate a radiomics nomogram for predicting stroke recurrence in symptomatic intracranial atherosclerotic stenosis (SICAS). Methods: The data of 156 patients with SICAS were obtained from the hospital database. Those with and without stroke recurrence were identified. The 156 patients were separated into a training (n = 110) and a validation cohort (n = 46). Baseline clinical data were collected from our medical records, and plaque radiological features were extracted from vascular wall high-resolution imaging (VW-HRMRI). The imaging sequences included 3D-T1WI-VISTA, T2WI, and 3D-T1WI-VISTA enhanced imaging. Least absolute shrinkage and selection operator (LASSO) analysis was used to select the radiomics features associated with stroke recurrence. Then, multiple logistic regression analysis of clinical risk factors, radiological features, and radiomics signatures was performed, and a predictive nomogram was constructed to predict probability of stroke recurrence in SICAS. The performance of the nomogram was evaluated. Results: Diabetes mellitus, plaque burden, and enhancement ratio were independent risk factors for stroke recurrence (odds ratio [OR] = 1.24, 95% confidence interval [CI]: 1.04-3.79, P = 0.018; OR = 2.21, 95% CI: 1.39-3.52, P < 0.001; and OR = 1.94, 95% CI: 1.27-3.09, P < 0.001). Five features of 3D-T1WI-VISTA, six features of T2WI, and nine features of 3D-T1WI-VISTA enhanced images were associated with stroke recurrence. The radiomics signature in 3D-T1WI-VISTA enhanced images was superior to the radiomics signature of the other two sequences for predicting stroke recurrence in both the training cohort (area under the curve [AUC], 0.790, 95% CI: 0.669-0.894) and the validation cohort (AUC, 0.779, 95% CI: 0.620-0.853). The combination of clinical risk factors, radiological features, and radiomics signature had the best predictive value (AUC, 0.899, 95% CI: 0.844-0.936, in the training cohort; AUC, 0.843, 95% CI: 0.761-0.897, in the validation cohort). The C-index of the nomogram was 0.880 (95% CI: 0.805-0.934) and 0.817 (95% CI: 0.795-0.948), respectively, in the training and validation cohorts. The decision curve analysis further confirmed that the radiomics nomogram had good clinical applicability, with net benefit of 0.458. Conclusion: The radiomics nomogram appears to be a reliable tool for predicting stroke recurrence in patients with SICAS.