Predictive value of magnetic resonance imaging-based texture analysis for hemorrhage transformation in large cerebral infarction - Zhai, Heng; Liu, Zhijun; Wu, Sheng; Cao, Ziqin; Xu, Yan; Lv, Yinzhang (Frontiers in Neuroscience, 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 still remains controversial. 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 (n = 44) and non-HT group (n = 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 for detremining the predictive performance of textural features, with HT as the outcome measure. 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) was significantly higher in patients with HT than those without HT. Among the textural parameters extracted from DWI images, 6 parameters (f2, f3, f4, f5, f10, and f11) differs significantly between the two groups (p < 0.05). Moreover, 5 of 6 parameters (f2, f3, f5, f10, and f11) have good predictive porformances 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.