Xie Gang, Li Ting, Ren Yitao, Wang Danni, Tang Wuli, Li Junlin, Li Kang
North Sichuan Medical College, Nanchong, China.
Department of Radiology, Chongqing General Hospital, Chongqing, China.
Front Neurosci. 2022 Sep 21;16:1002717. doi: 10.3389/fnins.2022.1002717. eCollection 2022.
To develop and validate a model based on the radiomics features of the infarct areas on non-contrast-enhanced CT to predict hemorrhagic transformation (HT) in acute ischemic stroke.
A total of 118 patients diagnosed with acute ischemic stroke in two centers from January 2019 to February 2022 were included. The radiomics features of infarcted areas on non-contrast-enhanced CT were extracted using 3D-Slicer. A univariate analysis and the least absolute shrinkage and selection operator (LASSO) were used to select features, and the radiomics score (Rad-score) was then constructed. The predictive model of HT was constructed by analyzing the Rad-score and clinical and imaging features in the training cohort, and it was verified in the validation cohort. The model was evaluated with the receiver operating characteristic curve, calibration curve and decision curve, and the prediction performance of the model in different scenarios was further discussed hierarchically.
Of the 118 patients, 52 developed HT, including 21 cases of hemorrhagic infarct (HI) and 31 cases of parenchymal hematoma (PH). The Rad-score was constructed from five radiomics features and was the only independent predictor for HT. The predictive model was constructed from the Rad-score. The area under the curve (AUCs) of the model for predicting HT in the training and validation cohorts were 0.845 and 0.750, respectively. Calibration curve and decision curve analyses showed that the model performed well. Further analysis found that the model predicted HT for different infarct sizes or treatment methods in the training and validation cohorts with 78.3 and 71.4% accuracy, respectively. For all samples, the model predicted an AUC of 0.754 for HT in patients within 4.5 h since stroke onset, and predicted an AUC of 0.648 for PH.
This model, which was based on CT radiomics features, could help to predict HT in the setting of acute ischemic stroke for any infarct size and provide guiding suggestions for clinical treatment and prognosis evaluation.
基于非增强CT梗死区域的影像组学特征开发并验证一个模型，以预测急性缺血性卒中的出血性转化（HT）。
纳入2019年1月至2022年2月在两个中心诊断为急性缺血性卒中的118例患者。使用3D-Slicer提取非增强CT梗死区域的影像组学特征。采用单因素分析和最小绝对收缩和选择算子（LASSO）进行特征选择，然后构建影像组学评分（Rad-score）。通过分析训练队列中的Rad-score以及临床和影像特征构建HT预测模型，并在验证队列中进行验证。采用受试者操作特征曲线、校准曲线和决策曲线对模型进行评估，并进一步分层讨论模型在不同场景下的预测性能。
118例患者中，52例发生HT，包括21例出血性梗死（HI）和31例实质内血肿（PH）。Rad-score由五个影像组学特征构建而成，是HT的唯一独立预测因子。根据Rad-score构建预测模型。该模型在训练队列和验证队列中预测HT的曲线下面积（AUC）分别为0.845和0.750。校准曲线和决策曲线分析表明该模型性能良好。进一步分析发现，该模型在训练队列和验证队列中分别以78.3%和71.4%的准确率预测不同梗死大小或治疗方法的HT。对于所有样本，该模型预测卒中发作后4.5小时内患者HT的AUC为0.754，预测PH的AUC为0.648。
该基于CT影像组学特征的模型有助于预测任何梗死大小的急性缺血性卒中患者的HT，并为临床治疗和预后评估提供指导建议。
