摘要
目的建立基于增强CT的影像组学模型,评估其鉴别肾透明细胞癌(ccRCC)与非透明细胞癌(non-ccRCC)的应用价值。方法将147例ccRCC及32例non-ccRCC患者随机分为训练集125例和测试集54例。将所有患者的增强CT资料导入ITK-SNAP软件,手动勾画ROI,获得16个特征,分别建立基于特征的随机森林(RF)模型和逻辑回归(LR)模型,采用ROC曲线观察模型对ccRCC的诊断效能。结果训练集RF模型诊断ccRCC的AUC为0.96(P<0.05),特异度为1.00,敏感度0.83;LR模型诊断ccRCC的AUC为0.96(P<0.05),特异度为1.00,敏感度为0.83。测试集RF模型诊断ccRCC的AUC为0.96(P<0.05),特异度为1.00,敏感度为0.89;LR模型诊断ccRCC的AUC为0.88(P<0.05),特异度为0.90,敏感度为0.77。结论基于增强CT影像组学模型可用于鉴别ccRCC与non-ccRCC;RF模型诊断价值较LR模型更高。
Objective To establish radiomics models based on enhanced CT, and to explore the value of the models for distinguishing renal clear cell carcinoma(ccRCC) and non-clear cell renal cell carcinoma(non-ccRCC). Methods Totally 147 patients with ccRCC and 32 patients with non-ccRCC were randomly divided into training set(n=125) and testing set(n=54). Enhanced CT data were imported into ITK-SNAP software, and ROI was manually delineated to obtain 16 features. Random Forest(RF) model and Logistic Regression(LR) model based on features were established, respectively. ROC curve was used to observe the diagnostic efficiency of the models for ccRCC. Results In the training set, RF model diagnosed ccRCC with AUC of 0.96(P<0.05) specificity of 1.00, and sensitivity of 0.83, while LR model diagnosed ccRCC with AUC of 0.96(P<0.05), specificity of 1.00, and sensitivity of 0.83. In the testing set, RF model diagnosed ccRCC with AUC of 0.96(P<0.05), specificity of 1.00, and sensitivity of 0.89, while LR model diagnosed ccRCC with AUC of 0.88(P<0.05), specificity of 0.90, and sensitivity of 0.77. Conclusion Radiomics models based on enhanced CT can be used for identifying ccRCC from non-ccRCC. RF model has higher diagnostic value than LR model.
作者
王平
裴旭
殷小平
任嘉梁
郭建党
赵珍珍
赵莹佳
WANG Ping;PEI Xu;YIN Xiaoping;REN Jialiang;GUO Jiandang;ZHAO Zhenzhen;ZHAO Yingjia(CT-MRI Division,Affiliated Hospital of Hebei University,Baoding 071000,China;Medical College,Hebei University,Baoding 071000,China;Key Laboratory of Cancer Radiotherapy and Chemotherapy Mechanism and Regulations of Hebei Province,Baoding 071000,China;General Electric PharmaceuticalCo.,Ltd.,Shanghai 210000,China)
出处
《中国医学影像技术》
CSCD
北大核心
2019年第11期1689-1692,共4页
Chinese Journal of Medical Imaging Technology
基金
河北省研究生创新资助项目(hbu2019ss036)
保定市科技计划项目(18ZF182)
