Gong Chundan, Liu Yun, Ma Wei, Jing Yang, Liu Li, Huang Yan, Yang Jinlin, Feng Chen, Fang Yuan, Fang Weidong
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Department of Radiology, The People's Hospital of Yubei District of Chongqing City, Chongqing, China.
Front Neurol. 2024 Dec 23;15:1492089. doi: 10.3389/fneur.2024.1492089. eCollection 2024.
To establish and validate a model based on hyperdense middle cerebral artery sign (HMCAS) radiomics features for predicting hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) after endovascular treatment (EVT).
Patients with AIS who presented with HMCAS on non-contrast computed tomography (NCCT) at admission and underwent EVT at three comprehensive hospitals between June 2020 and January 2024 were recruited for this retrospective study. A radiomics model was constructed using the HMCAS radiomics features most strongly associated with HT. In addition, clinical and radiological independent factors associated with HT were identified. Subsequently, a combined model incorporating radiomics features and independent risk factors was developed via multivariate logistic regression and presented as a nomogram. The models were evaluated via receiver operating characteristic curve, calibration curve, and decision curve analysis.
Of the 118 patients, 71 (60.17%) developed HT. The area under the curve (AUC) of the radiomics model was 0.873 (95% CI 0.797-0.935) in the training cohort and 0.851 (95%CI 0.721-0.942) in the test cohort. The Alberta Stroke Program Early CT score (ASPECTS) was the only independent predictor among 24 clinical and 4 radiological variables. The combined model further improved the predictive performance, with an AUC of 0.911 (95%CI 0.850-0.960) in the training cohort and 0.877 (95%CI 0.753-0.960) in the test cohort. Decision curve analysis demonstrated that the combined model had greater clinical utility for predicting HT.
HMCAS-based radiomics is expected to be a reliable tool for predicting HT risk stratification in AIS patients after EVT.
建立并验证基于大脑中动脉高密度征(HMCAS)影像组学特征的模型,以预测急性缺血性卒中(AIS)患者血管内治疗(EVT)后出血性转化(HT)的发生情况。
本回顾性研究纳入了2020年6月至2024年1月期间在三家综合医院就诊、入院时非增强计算机断层扫描(NCCT)显示有HMCAS且接受了EVT的AIS患者。利用与HT相关性最强的HMCAS影像组学特征构建影像组学模型。此外,确定与HT相关的临床和影像学独立因素。随后,通过多因素逻辑回归建立了一个结合影像组学特征和独立危险因素的联合模型,并以列线图形式呈现。通过受试者工作特征曲线、校准曲线和决策曲线分析对模型进行评估。
118例患者中,71例(60.17%)发生了HT。影像组学模型在训练队列中的曲线下面积(AUC)为0.873(95%CI 0.797 - 0.935),在测试队列中为0.851(95%CI 0.721 - 0.942)。阿尔伯塔卒中项目早期CT评分(ASPECTS)是24个临床变量和4个影像学变量中唯一的独立预测因素。联合模型进一步提高了预测性能,在训练队列中的AUC为0.911(95%CI 0.850 - 0.960),在测试队列中为0.877(95%CI 0.753 - 0.960)。决策曲线分析表明,联合模型在预测HT方面具有更大的临床实用性。
基于HMCAS的影像组学有望成为预测AIS患者EVT后HT风险分层的可靠工具。