Abstract: Objective The aim of the study was to explore the feasibility of predicting early hematoma enlargement of hypertensive intracerebral hemorrhage based on texture analysis of non-contrast CT images. Methods A retrospective analysis of the CT image data of 74 patients with hypertensive intracerebral hemorrhage in our hospital, the patients were divided into two groups with （group A） and without（group B）hematoma enlargement. The Omni-Kinetics software was used to extract the texture features of the hematoma region in the first CT images of the two groups patients for comparison, incorporate statistically different parameters into the binary logistics regression analysis, and perform receiver operating characteristic curves （ROC） for all statistically significant parameters and the regression model analyze diagnostic performance. Results The differences in texture characteristics of the two groups in std-Deviation, Variance, Entropy, UPP, sumEntropy, Compactness, MaxSize, Sphericity were statistically significant （P<0.05）. The prediction model of the binary logistics regression equation was： Logit（P）=-43.929+sumEntropy×81.731+Compactness×-1.300, the area under the ROC curve of Logit（P）was 0.921, when the threshold was 0.694, the sensitivity and specificity were 90.5% and 83.0%. Conclusion Texture analysis based on non-contrast CT images is helpful to predict the early expansion of hematoma in hypertensive intracerebral hemorrhage, and the predictive model has the best diagnostic efficiency.

Key words: texture analysis, intracerebral hemorrhage, tomography, X-ray computer

摘要： 目的 探讨基于CT平扫图像纹理分析预测高血压脑出血早期血肿扩大的可行性。方法 回顾分析我院74例高血压脑出血患者的CT图像资料,根据血肿是否扩大将患者分两组,A组：血肿扩大,B组：血肿无扩大。利用Omni-Kinetics软件提取两组患者首诊CT图像血肿区域纹理特征进行比较,将具有统计学差异的参数纳入二元Logistics回归分析,对所有具有统计学意义的参数与回归模型进行受试者工作特征曲线（ROC）分析诊断效能。结果 两组患者纹理特征std-Deviation、Variance、Entropy、UPP、sumEntropy、Compactness、MaxSize、Sphericity差异具有统计学意义（P<0.05）,二元logistics回归方程预测模型为：Logit（P）=-43.929+ umEntropy×81.731+Compactness×-1.300,Logit（P）的ROC曲线下面积为0.921,取阈值为0.694时,敏感度及特异性分别为（90.5%、83.0%）。结论 基于CT平扫图像纹理分析有助于预测高血压脑出血早期血肿扩大,且预测模型诊断效能最佳。

关键词: 纹理分析, 脑出血, 体层摄影术, X线计算机