CT纹理特征评估胰腺导管腺癌的分化程度

Evaluation of histopathological differentiation in pancreatic ductal adenocarcinoma by texture analysis of CT

目的:探究基于术前增强纹理特征构建模型对评估胰腺导管腺癌(pancreatic ductal adenocarcinoma,PDAC)分化程度的价值。方法:回顾性收集2017年1月—2020年10月66例PDAC患者的病例资料,另外34例来自其他医院的PDAC患者被用于外部验证,根据术后病理结果分为高分化、中-低分化两组,分别记录患者的性别、年龄、肿瘤部位、肿瘤最大径、肿瘤强化程度、血管侵犯情况等临床及常规影像特征,进行单因素回归分析。采用ITK-SNAP软件勾画CT检查动、静脉期图像的感兴趣区(ROI),并提取图像纹理特征。利用单因素分析和二元Logistic回归筛选独立预测因子并构建CT纹理特征模型,将训练组建立的预测模型直接应用于外部验证组,检验模型的准确度。应用受试者工作特征曲线(ROC)的曲线下面积(AUC)评价预测模型诊断价值。结果:基于动脉期及静脉期分别筛选出1个和2个纹理特征,分别为运行熵(run entropy)、区域百分比(zone percentage)和区域大小不均匀性(size-zone non-uniformity),其成为具有特征性的预测参数并分别构建了预测模型,基于CT动脉期纹理特征模型在训练组和验证组的AUC、灵敏度及特异度分别为0.716、0.581、0.824和0.722、0.600、0.765;基于CT静脉期纹理特征模型在训练组和验证组的AUC、灵敏度及特异度分别为0.895、0.781、0.882和0.873、0.722、0.929。结论:CT增强图像纹理特征在高分化、中-低分化PDAC之间存在差异,给术前评估PDAC恶性程度提供了新的方法。

Objective:To investigate the value of the texture analysis based on preoperative enhanced CT in predicting the histopathological differentiation of pancreatic ductal adenocarcinoma(PDAC).Methods:Pathological data of 66 patients with PDAC from January 2017 to October 2020 were retrospectively collected,other 34 PDAC patients from other hospital were used for external validation cohort,and they were divided into two groups according to postoperative pathological results:high differentiation and moderately-poorly differentiation.The clinical and conventional imaging characteristics such as gender,age,tumor site,maximum tumor diameter,tumor enhancement and vascular invasion were recorded for univariate regression analysis.ROI based on both arterial phase and venous phase of the preoperative enhanced CT was automatically drawn by ITK-SNAP software and texture features were extracted.Univariate analysis was used to compare the texture features between the two groups,and the texture features with statistical difference were included in binary logistic regression model,and the prediction model of arterial phase and venous phase were established respectively.The prediction model established by the training group is directly applied to the external validation group.The AUC values of ROC were used to evaluate the diagnostic value of prediction model.Results:One and two texture features were selected to construct prediction model respectively based on the CT arterial and venous phase,including run entropy,zone percentage and size-zone non-uniformity.The AUC,sensitivity and specificity were 0.716,0.581,0.824,0.722,0.600,and 0.765 in the training group and the validation group based on the CT arterial texture feature model respectively.The AUC,sensitivity and specificity of the CT venous texture feature model in the training group and the validation group were 0.895,0.781,0.882,0.873,0.722,0.929,respectively.Conclusion:There are differences in the characteristic parameters of texture analysis of CT enhanced images between highly and moderately-poorly differentiated pancreatic ductal adenocarcinoma,providing a new method for preoperative evaluation of the malignant degree of pancreatic ductal adenocarcinoma carcinoma.