Radiomics Nomogram for Predicting Stroke Recurrence in Symptomatic Intracranial Atherosclerotic Stenosis

Abstract

ObjectiveTo develop and validate a radiomics nomogram for predicting stroke recurrence in symptomatic intracranial atherosclerotic stenosis (SICAS).MethodsThe 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 cohort (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 were used to select the radiomics features associated with stroke recurrence. Then, multiple logistic regression analysis of clinical risk factors, radiological features, and radiomics signatures were performed, and a predictive nomogram was constructed to predict the probability of stroke recurrence in SICAS. The performance of the nomogram was evaluated.ResultsDiabetes 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 = 1.76, per 10% increase, 95% CI, 1.28–2.41, 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, 0.803, 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 a net benefit of 0.458.ConclusionThe radiomics features were helpful to predict stroke recurrence in patients with SICAS. The nomogram constructed by combining clinical high-risk factors, plaque radiological features, and radiomics features is a reliable tool for the individualized risk assessment of predicting the recurrence of SICAS stroke.

Distinguishing Intracranial Diabetes-Related Atherosclerotic Plaques: A High-Resolution Magnetic Resonance Imaging-Based Radiomics Study

“…Radiomics-based machine learning for coronary CTA outperforms expert visual assessment in identifying advanced atherosclerotic lesions [21]. Radiomics studies related to the assessment of intracranial atherosclerotic plaques by hrMRI have focused on differentiating between culprit and nonculprit lesions [22], identifying the different periods of plaque formation, and predicting stroke recurrence [23].…”

Optimal Medical Management of Atherosclerotic Intracranial Stenosis

“…Patients with diabetes are known to be at higher risk of recurrent stroke due to sICAS, 17,[40][41][42][43][44][45] but there is a lack of compelling evidence to suggest that improved glycemic control results in better outcomes in patients with sICAS in large randomized controlled trials. A retrospective vessel wall magnetic resonance imaging study of 156 patients with 50% to 99% sICAS found diabetes (OR, 1.24 [95% CI, 1.04-3.79]; P=0.018) and enhancement ratios of culprit plaques (OR, 1.94 [95% CI, 1.27-3.09]; P<0.001) to be independently associated with risk of recurrent ischemic stroke.…”

“…A retrospective vessel wall magnetic resonance imaging study of 156 patients with 50% to 99% sICAS found diabetes (OR, 1.24 [95% CI, 1.04–3.79]; P =0.018) and enhancement ratios of culprit plaques (OR, 1.94 [95% CI, 1.27–3.09]; P <0.001) to be independently associated with risk of recurrent ischemic stroke. 44 Another vessel wall imaging magnetic resonance imaging study in 225 patients with sICAS found hemoglobin A1C to be an independent risk factor for higher plaque enhancement ratios, 46 perhaps demonstrating a link between poor glycemic control and high-risk plaques. Management of diabetes to a goal of glycosylated hemoglobin A1C level of ≤7 is recommended for most patients with stroke to reduce risk of microvascular complications, 14 but the goal and treatment choices should be individualized.…”

A CT-based radiomics nomogram for classification of intraparenchymal hyperdense areas in patients with acute ischemic stroke following mechanical thrombectomy treatment

“…Radiomics can facilitate better clinical decision by improving the process of detecting heterogeneous findings without visible abnormalities in medical images through high-throughput quantitative analysis of statistical features (Gillies et al, 2016 ). Successful applications of radiomics in acute stroke have been reported in prediction of the hematoma expansion (Ma et al, 2019 ; Xie et al, 2020 ; Liu et al, 2021 ; Song et al, 2021 ), successful recanalization (Qiu et al, 2019 ; Hofmeister et al, 2020 ), recurrence (Tang et al, 2022 ) and functional outcome (Haider et al, 2021 ; Quan et al, 2021 ; Wang et al, 2021 ). The discrimination of hematomas etiologies (Zhang et al, 2019 ; Nawabi et al, 2020 ) using radiomics analysis had been reported as well.…”