基于CT影像组学特征预测低分化胆囊癌的价值  

Value of CT-based radiomics in predicting poorly differentiated gallbladder carcinoma

作　　者：霍文礼 寇雪纯 杨敏[1,3] 梁挺 刘军[1] Huo Wenli;Kou Xuechun;Yang Min;Liang Ting;Liu Jun(Department of Medical Imaging,The First Affiliated Hospital of Xi’an Jiaotong University,Xi’an 710061;Shaanxi Healthcare Group Co.,Ltd,Xi’an 710100;Department of Radiology,521 Hospital of Norinco Group,Xi’an 710065,China)

机构地区：[1]西安交通大学第一附属医院医学影像科,西安710061 [2]陕西健康医疗集团有限公司,西安710100 [3]兵器工业五二一医院CT科,西安710065

出　　处：《解剖学杂志》2024年第1期11-14,51,共5页Chinese Journal of Anatomy

摘　　要：目的:探讨多相CT影像组学特征在预测低分化胆囊癌中的价值。方法:回顾性分析本院行根治性胆囊切除术后经病理证实的胆囊癌,根据病理结果分为低分化组(占比44.5%)和非低分化组(占比55.5%)。应用3D Slicer软件手动勾画感兴趣区(ROI),分别提取动脉期及静脉期影像组学特征。使用互信息法筛选影像组学特征,将训练集三步降维法筛选的双期影像组学特征拟合至K-邻近算法构建胆囊癌低分化预测模型,测试集用于模型预测效能评价。绘制受试者工作特征(ROC)曲线,通过比较曲线下面积(AUC),确定影像组学特征对低分化胆囊癌的预测效能。结果:筛选出双期特征各1502个,应用三步降维法提取6个特征,即大面积强调、大区域高灰度强调、熵、均值、均方根、第10百分位。预测模型结果显示,训练集AUC为0.83(灵敏度0.76,特异度0.71),测试集AUC为0.68(灵敏度0.67,特异度0.60)。结论:双相CT影像组学特征对低分化胆囊癌的分级具有较好的预测价值,并具有可重复性。Objective:To explore the value of multiphase CT-based radiomics in predicting poorly differentiated gallbladder carcinoma.Methods:Patients with gallbladder carcinoma confirmed by pathological examinations after radical resection in our hospital were analyzed retrospectively.According to pathological examinations,they were divided into low differentiation(44.5%)and non-low differentiation groups(55.5%),respectively.The region of interest(ROI)was manually delineated by using 3D Slicer software,and the radiomics features of arterial phase and venous phase were extracted respectively and screened by mutual information regression method finally.The training set screened by the three-step dimensionality reduction method was fitted to the K-nearest neighbor algorithm to construct the low-differentiation prediction model of gallbladder carcinoma.The testing set was used to evaluate the model prediction efficiency.The receiver operating characteristic(ROC)curves were drawn,and the area under the curve(AUC)was conducted to determine the predictive power of radiomic features in distinguishing poorly differentiated gallbladder carcinoma.Results:A total of 1502 dual-phase radiomics features were screened out,and six features were extracted by three-step dimensionality reduction,large area emphasis,large area high gray level emphasis,run entropy,mean value,root mean squared,and 10 percentile.The results of the prediction model,the training set AUC was 0.83(sensitivity 0.76,specificity 0.71),and the testing set AUC was 0.68(sensitivity 0.67,specificity 0.60).Conclusion:The dural-phase CT-based radiomics features have certain value in in predicting the grading of poorly differentiated gallbladder carcinoma and can be repeated well.

关 键 词：胆囊癌 影像组学 CT 预测模型 病理分级 

分 类 号：R365[医药卫生—病理学]