Zhai Heng, Liu Zhijun, Wu Sheng, Cao Ziqin, Xu Yan, Lv Yinzhang
Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Neurosci. 2022 Jul 22;16:923708. doi: 10.3389/fnins.2022.923708. eCollection 2022.
Massive cerebral infarction (MCI) is a devastating condition and associated with high rate of morbidity and mortality. Hemorrhagic transformation (HT) is a common complication after acute MCI, and often results in poor outcomes. Although several predictors of HT have been identified in acute ischemic stroke (AIS), the association between the predictors and HT remains controversial. Therefore, we aim to explore the value of texture analysis on magnetic resonance image (MRI) for predicting HT after acute MCI. This retrospective study included a total of 98 consecutive patients who were admitted for acute MCI between January 2019 and October 2020. Patients were divided into the HT group ( = 44) and non-HT group ( = 54) according to the follow-up computed tomography (CT) images. A total of 11 quantitative texture features derived from images of diffusion-weighted image (DWI) or T2-weighted-Fluid-Attenuated Inversion Recovery (T2/FLAIR) were extracted for each patient. Receiver operating characteristic (ROC) analysis were performed to determine the predictive performance of textural features, with HT as the outcome measurement. There was no significant difference in the baseline demographic and clinical characteristics between the two groups. The distribution of atrial fibrillation and National Institutes of Health Stroke Scale (NIHSS) were significantly higher in patients with HT than those without HT. Among the textural parameters extracted from DWI images, six parameters, f2 (contrast), f3 (correlation), f4 (sum of squares), f5 (inverse difference moment), f10 (difference variance), and f11 (difference entropy), differs significantly between the two groups ( < 0.05). Moreover, five of six parameters (f2, f3, f5, f10, and f11) have good predictive performances of HT with the area under the ROC curve (AUC) values of 0.795, 0.779, 0.791, 0.780, and 0.797, respectively. However, the texture features f2, f3, and f10 in T2/FLAIR images were the only three significant predictors of HT in patients with acute MCI, but with a relatively low AUC values of 0.652, 0.652, and 0.670, respectively. In summary, our preliminary results showed DWI-based texture analysis has a good predictive validity for HT in patients with acute MCI. Multiparametric MRI texture analysis model should be developed to improve the prediction performance of HT following acute MCI.
大面积脑梗死（MCI）是一种严重的疾病，与高发病率和死亡率相关。出血性转化（HT）是急性MCI后的常见并发症，常导致不良预后。虽然在急性缺血性卒中（AIS）中已确定了几个HT的预测因素，但这些预测因素与HT之间的关联仍存在争议。因此，我们旨在探讨磁共振成像（MRI）纹理分析对预测急性MCI后HT的价值。这项回顾性研究共纳入了98例在2019年1月至2020年10月期间因急性MCI入院的连续患者。根据随访计算机断层扫描（CT）图像，将患者分为HT组（n = 44）和非HT组（n = 54）。为每位患者提取了总共11个从扩散加权图像（DWI）或T2加权液体衰减反转恢复（T2/FLAIR）图像得出的定量纹理特征。进行受试者操作特征（ROC）分析以确定纹理特征的预测性能，以HT作为结局指标。两组之间的基线人口统计学和临床特征无显著差异。HT患者的心房颤动和美国国立卫生研究院卒中量表（NIHSS）分布显著高于非HT患者。在从DWI图像中提取的纹理参数中，六个参数，f2（对比度）、f3（相关性）、f4（平方和）、f5（逆差矩）、f10（差异方差）和f11（差异熵），在两组之间有显著差异（P < 0.05）。此外，六个参数中的五个（f2、f3、f5、f10和f11）对HT具有良好的预测性能，ROC曲线下面积（AUC）值分别为0.795、0.779、0.791、0.780和0.797。然而，T2/FLAIR图像中的纹理特征f2、f3和f10是急性MCI患者中HT的仅有的三个显著预测因素，但AUC值相对较低，分别为0.652、0.652和0.670。总之，我们的初步结果表明基于DWI的纹理分析对急性MCI患者的HT具有良好的预测效度。应开发多参数MRI纹理分析模型以提高急性MCI后HT的预测性能。
