摘要
目的探讨基于CT影像组学构建肾透明细胞癌(ccRCC)WHO/国际泌尿病理学会(ISUP)分级预测模型的临床意义。方法回顾性分析陕西中医药大学附属医院2014年3月至2018年12月行能谱CT增强扫描并经手术或穿刺活检病理确诊的104例ccRCC患者的临床资料。其中男70例,女34例;年龄(61.2±11.7)岁。肿瘤直径(139.6±28.5)mm;肿瘤位于右肾50例,左肾54例;有包膜72例;存在静脉瘤栓17例;伴有淋巴结肿大37例。采用分层抽样法按照7∶3比例随机将患者分为训练组(73例)和验证组(31例)。根据2016版肾癌WHO/ISUP病理分级标准重新诊断并分级,将Ⅰ~Ⅱ级定义为低级别,Ⅲ~Ⅳ级定义为高级别。在CT增强扫描皮质期图像中计算ccRCC的影像组学特征。采用LASSO回归对训练组影像组学特征进行降维,并建立影像组学风险评分。采用二元logistic回归构建预测模型。采用Bootstrap法对训练组和验证组模型进行内部验证,分别计算受试者工作特征(ROC)曲线下面积(AUC)、敏感性及特异性。采用Hosmer-Lemeshow拟合优度检验评价模型的校准度。结果ccRCC影像组学特征降维后建立影像组学风险评分。训练组低、高级别风险评分分别为-2.49±1.73和1.23±2.17,差异有统计学意义(t=-7.785,P<0.01)。多因素二元logistic回归结果表明仅影像组学风险评分是预测ccRCC WHO/ISUP分级的独立危险因素(OR=3.576,95%CI 1.964~6.513)。预测模型为Y=1/[1+exp(-Z)],Z=1.274×影像组学风险评分+0.072。Bootstrap法内部验证结果表明训练组的AUC为0.940(95%CI 0.883~0.998),敏感性为95.5%,特异性为88.2%。Hosmer-Lemeshow拟合优度检验结果表明该预测模型的校准度较好(χ2=4.463,P=0.813)。验证组低、高级别风险评分分别为-2.27±2.02和0.82±2.08,差异有统计学意义(t=-3.832,P<0.01)。预测模型在验证组的AUC为0.859(95%CI 0.723~0.995),敏感性为77.8%,特异性为81.8%。Hosmer-Lemeshow拟合优度检验结果表明,该预测模型的校准度较好(χ2=14.554,P=0.068)。结论基于ccRCC CT增强扫描皮质期图像影像组学构建的预测模型,对预测ccRCC的WHO/ISUP分级具有较高诊断效能,可为患者的治疗及预后提供参考依据。
Objective A predictive model of WHO/ISUP grading of renal clear cell carcinoma was constructed based on CT radiomics.Methods The clinical data of 104 patients with ccRCC confirmed by operation or biopsy from March 2014 to December 2018 in the Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine were retrospectively analyzed.There were 70 males and 34 females,and the age was 61.2±11.7 years.The patients were randomly divided into development cohort(73 cases)and validation cohort(31 cases)by stratified sampling according to 7∶3 ratio.According to the WHO/ISUP pathological grading criteria of renal cancer in 2016,ⅠandⅡwere defined as low-grade group,ⅢandⅣwere defined as high-grade group.The radiomics features of ccRCC were calculated in cortical phase images of CT enhanced scanning.LASSO regression was used to reduce the radiomics feature dimensionality in the training group,and to establish radiomics risk scores.The binary logistic regression was used to build the prediction model,which was used in the validation group.Bootstrap method was used to validate the model of training and validation group.AUC,sensitivity and specificity were calculated respectively.Hosmer-Lemeshow goodness-of-fit test was used to evaluate model calibration degree.Results After dimensionality reduction,the radiomics risk score of ccRCC was established.The low and high-level risk scores of the training group were-2.49±1.73 and 1.23±2.17,with significant difference(t=-7.785,P<0.01).The binary logistic regression multivariate analysis showed that the radiomics risk score was an independent risk factor in identifying low or high-grade ccRCC with odds ratio of(OR=3.576,95%CI 1.964~6.513).The predictive model was Y=1/[1+exp(-Z)],Z=1.274×radiomics risk score+0.072.The AUC of radiomics risk score in training group was 0.940(95%CI 0.883-0.998)with 95.5%sensitivity and 88.2%specificity after internal verification by Bootstrap method,and good Hosmer-Lemeshow goodness-of-fit test(χ2=4.463,P>0.05).The low and high-level risk scores of the Validation group were-2.27±2.02 and 0.82±2.08,with significant difference(t=-3.832,P<0.01).The AUC in validation group was 0.859(95%CI 0.723-0.995)with 77.8%sensitivity and 81.8%specificity,and with good Hosmer-Lemeshow goodness-of-fit test(χ2=14.554,P=0.068)as well.Conclusions The prediction model based on CT radiomics has high accuracy in predicting high or low grade of ccRCC.
作者
韩冬
贺太平
吴宏培
于楠
张喜荣
任革
于勇
Han Dong;He Taiping;Wu Hongpei;Yu Nan;Zhang Xirong;Ren Ge;Yu Yong(Department of Radiology,Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine,Xianyang 712021,China;Department of Pathology,Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine,Xianyang 712021,China)
出处
《中华泌尿外科杂志》
CAS
CSCD
北大核心
2019年第12期889-894,共6页
Chinese Journal of Urology
基金
陕西中医药大学学科创新团队建设项目(2019-YS04)。
关键词
癌
肾细胞
影像组学
病理分级
预测模型
Carcinoma
renal cell
Radiomics
Pathological grading
Predictive model
