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Identifying high-risk intracranial plaques is significant for the treatment and prevention of stroke.To develop a high-risk plaque model using three-dimensional (3D) high-resolution magnetic resonance imaging (HRMRI) based radiomics features and machine learning.136 patients with documented symptomatic intracranial artery stenosis and available HRMRI data were included. Among these patients, 136 and 92 plaques were identified as symptomatic and asymptomatic plaques, respectively. A conventional model was developed by recording and quantifying the radiological plaque characteristics. Radiomics features from T1-weighted images (T1WI) and contrast-enhanced T1WI (CE-T1WI) were used to construct a high-risk plaque model with linear support vector classification (linear SVC). The radiological and radiomics features were combined to build a combined model. Receiver operating characteristic (ROC) curves were used to evaluate these models.Plaque length, burden, and enhancement were independently associated with clinical symptoms and were included in the conventional model, which had an AUC of 0.853 vs. 0.837 in the training and test sets. While the radiomics and the combined model showed an improved AUC: 0.923 vs. 0.925 for the training sets and 0.906 vs. 0.903 in the test sets. Both the radiomics model (p = 0.024, p = 0.018) and combined model (p = 0.042, p = 0.049) outperformed the conventional model in the two sets, whereas the performance of the combined model was not significantly different from that of the radiomics model in the two sets (p = 0.583 and p = 0.606).The radiomics model based on 3D HRMRI can accurately differentiate symptomatic from asymptomatic intracranial arterial plaques and significantly outperforms the conventional model.

Hongquan Guo,1,&ast; Wei Xu,1,2,&ast; Xiaohao Zhang,3,&ast; Shuai Zhang,4 Zheng Dai,5 Shun Li,6 Yi Xie,3 Yingle Li,1 Jianzhong Xue,7 Xinfeng Liu1 1Department of Neurology, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, Jiangsu, 210002, People’s Republic of China; 2Department of Neurology, Affiliated Changsha Central Hospital, University of South China, Changsha, Hunan, 410000, People’s Republic of China; 3Department of Neurology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, People’s Republic of China; 4Department of Neurology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, 225001, People’s Republic of China; 5Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, Jiangsu, 214023, People’s Republic of China; 6Department of Cerebrovascular Disease Treatment Center, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, 210002, People’s Republic of China; 7Department of Neurology, Changshu No.2 People’s Hospital, Changshu, Jiangsu, 215500, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xinfeng LiuDepartment of Neurology, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, No. 305 East Zhongshan Road, Nanjing, 210002, Jiangsu Province, People’s Republic of ChinaTel +86 2584801861Fax +86 2584805169Email xfliu2@vip.163.comJianzhong XueDepartment of Neurology, Changshu No.2 People’s Hospital, No. 18 Taishan Road, Changshu, Jiangsu Province, People’s Republic of ChinaTel +86 13506247572Email xjz2021@sina.comBackground and Aims: A reliable predictive score system to identify the risk of symptomatic intracranial hemorrhage (sICH) after intravenous thrombolysis (IVT) in acute ischemic stroke patients is of great essence. We aimed to develop a nomogram for predicting the risk of sICH after IVT in Chinese patients.Methods: We recruited acute ischemic stroke patients who were treated with IVT from five advanced stroke centers in China from April 2014 to November 2020. sICH was diagnosed according to the European Cooperative Acute Stroke Study II (ECASS-II) definition. Multivariable logistic regression was performed to construct the best-fit nomogram. The discrimination and calibration of the nomogram were evaluated by the area under the receiver operating characteristic curve (AUC-ROC) and calibration plot.Results: A total of 1200 patients were enrolled, of whom 66 (5.5%) developed sICH. In the multivariate logistic regression model, atrial fibrillation (odds ratio [OR] 3.25; 95% confidence interval [CI], 1.89− 5.60; P < 0.001), baseline glucose level (OR, 1.13; 95% CI, 1.07− 1.20; P < 0.001), neutrophil to lymphocyte ratio (OR, 1.05; 95% CI, 1.01− 1.09; P = 0.024) and baseline National Institute of Health Stroke Scale (NIHSS) (OR, 1.07; 95% CI, 1.04− 1.10; P < 0.001) were independent predictors for sICH and were used to generate the nomogram. The nomogram demonstrated good discrimination as the AUC-ROC value was 0.788 (95% CI, 0.737– 0.840). The calibration plot revealed good calibration.Conclusion: The nomogram consisted of atrial fibrillation, baseline glucose level, neutrophil to lymphocyte ratio, and NIHSS score may predict the risk of sICH in Chinese acute ischemic stroke patients treated with IVT.Keywords: stroke, intravenous thrombolysis, symptomatic intracranial hemorrhage, nomogram, predict

We assessed the value of computed tomography (CT) and automated Alberta Stroke Program Early CT Score (ASPECTS) with net water uptake (NWU) to predict stroke onset time. Two-hundred forty stroke patients with anterior circulation large-vessel occlusion were included. CT-ASPECTS-NWU values were calculated by comparing the mean Hounsfield units of affected ASPECTS regions with unaffected contralateral regions. The correlation between ASPECTS-NWU and stroke onset to CT time was assessed. ASPECTS-NWU predictive values were calculated to identify a stroke onset to CT time of within 4.5/6 h. A correlation existed between stroke onset to CT time and ASPECTS-NWU (r = 0.65, p

Introduction: Imaging-based early warning indicators and feasible stratification of acute ischemic stroke (AIS) patients with hemorrhagic transformation (HT), especially high-risk patients with parenchymal hematoma (PH), are crucial in determining subsequent treatment strategies. This study combined automated ASPECTS software with noncontrast CT (NCCT) and CTA source image (CTASI) attenuation changes using Hounsfield unit (HU) values to predict HT and PH in patients with AIS. Materials and Methods: We retrospectively enrolled 172 consecutive patients with anterior circulation large-vessel occlusion between 2016 and 2020. Univariate and multivariate logistic regression and receiver operating characteristic (ROC) analyses were used to investigate the relationship between NCCT and CTASI-ASPECTS-HU, as well as other clinical and radiological parameters of HT and PH. Univariate and multivariate logistic regression analyses were performed to explore risk factors for HT or PH, and an ROC curve was used to evaluate their diagnostic values. Results: A multivariate analysis showed that CTASI-ASPECTS-HU and NIHSS score were independent predictors of HT (CTASI-ASPECTS-HU: odds ratio (OR), 2.22; 95% CI, 1.01–4.92; NIHSS: OR, 1.07; 95% CI, 1.02–1.13) and PH (CTASI-ASPECTS-HU: OR, 6.51; 95% CI, 2.29–18.50; NIHSS: OR, 1.07; 95% CI, 1.01–1.13). According to ROC analysis, CTASI-ASPECTS-HU >0.09 identified HT (area under the curve, 0.70; sensitivity, 70.15%; specificity, 61.90%), and CTASI-ASPECTS-HU >0.10 identified PH (area under the curve, 0.79; sensitivity, 76.19%; specificity, 73.33%). The area under the curve for predicting HT or PH increased when CTASI-ASPECTS-HU was combined with NIHSS score (HT: area under the curve, 0.74; sensitivity, 73.13%; specificity, 70.48%; PH: area under the curve, 0.81; sensitivity, 85.71%; specificity, 72.38%). Conclusion: CTASI-ASPECTS-HU is a reliable radiological predictor of HT and PH in patients with AIS. Its predictive efficacy is moderately improved when combined with NIHSS score.

We assessed the value of computed tomography (CT) and automated Alberta Stroke Program Early CT Score (ASPECTS) with net water uptake (NWU) to predict stroke onset time.Two-hundred forty stroke patients with anterior circulation large-vessel occlusion were included. CT-ASPECTS-NWU values were calculated by comparing the mean Hounsfield units of affected ASPECTS regions with unaffected contralateral regions. The correlation between ASPECTS-NWU and stroke onset to CT time was assessed. ASPECTS-NWU predictive values were calculated to identify a stroke onset to CT time of within 4.5/6 h.A correlation existed between stroke onset to CT time and ASPECTS-NWU (r = 0.65, p 0.001), which was affected by collateral status and infarct location. The area under the receiver operating characteristic (ROC) curve (AUC) for distinguishing a stroke onset to CT time of within 4.5 h was 0.837 (95% confidence interval [CI] 0.784-0.881; optimal cutoff 7%; sensitivity 87.10%; specificity 62.36%). The multi-index AUC was 0.884 (95% CI 0.837-0.922). The AUC for distinguishing a stroke onset to CT time of within 6 h was 0.836 (95% CI 0.783-0.880; optimal cutoff 9%; sensitivity 72.73%; specificity 81.16%). The multi-index AUC was 0.881 (95% CI 0.834-0.920).ASPECTS-NWU may be used to determine stroke onset time in patients with unwitnessed or wake-up stroke.

This study aimed to use the automated Alberta Stroke Program Early CT Score (ASPECTS) software to assess the value of different CT modalities (non-contrast CT, CT angiography [CTA]-arterial, CTA-venous, and arterial- and venous-phase mismatch-ASPECTS) in predicting the final infarct extent and clinical outcome in large-vessel occlusion stroke.This retrospective study included patients with large-vessel occlusion stroke who underwent reperfusion therapy during 2015 to 2019. Correlations between different CT-ASPECTS modalities and follow-up CT-ASPECTS and outcome were determined using Spearman rank correlation coefficient. Receiver operating characteristic curve analysis was used to assess the ability of different CT-ASPECTS modalities to identify patients with good outcomes.One hundred and thirty-five patients were included. We found almost-perfect correlation between CTA-venous-ASPECTS and follow-up CT-ASPECTS (r = 0.92; 95% CI: 0.89-0.95), better than that in other CT modalities. The 90-day modified Rankin scale (mRS) score substantially correlated with CTA-venous-ASPECTS (r = -0.64; 95% CI: -0.73 to -0.52). The ROC curve analysis showed CTA-venous-ASPECTS had the highest area under the curve (AUC: 0.82; 95% CI: 0.75-0.89; P 0.001), followed by mismatch-ASPECTS (AUC: 0.75; 95% CI: 0.65-0.85; P 0.001). When emphasizing the sensitivity for identifying patients with good outcomes, the best cut-off point of mismatch-ASPECTS was -3 with the highest sensitivity (91.30%).CTA-venous-ASPECTS is a reliable tool to predict the infarct extent and outcome. Furthermore, mismatch-ASPECTS may represent images in different angiographic phases and was sensitive for prognosis prediction.