基于CT平扫纹理分析的影像组学预测肾透明细胞癌WHO/ISUP分级的初步研究

目的:探讨基于肾脏CT平扫图像纹理分析的影像组学模型在预测肾透明细胞癌（ccRCC）WHO/国际泌尿病理学会（ISUP）病理分级中的价值。方法:回顾性分析2016年12月至2019年5月中国科学院大学附属肿瘤医院经手术病理证实且有明确病理分级的90例ccRCC患者，按照7∶3的比例将所有患者随机分为训练组（63例）及测...

A preliminary study of radiomics in predicting WHO/ISUP grading of clear cell renal cell carcinoma based on unenhanced CT texture analysis

Objective To investigate the value of radiomics based on unenhanced CT texture analysis in predicting the WHO/International Society of Urological Pathology(ISUP)grading of clear cell renal cell carcinoma(ccRCC).Methods Postoperative pathology-confirmed ccRCC subjects(n=90)who received CT scanning and had a definite pathological grading in Cancer Hospital of the University of Chinese Academy of Sciences were collected retrospectively from December 2016 to May 2019.The cases were randomly divided into training group(n=63)and test group(n=27)as a ratio of 7∶3.All cases were classified into low grade(gradesⅠandⅡ,n=57)and high grade(gradesⅢandⅣ,n=37)according to the new pathological grading(WHO/ISUP grading,version 2016)of renal carcinoma.3D-ROI segmentation was performed on unenhanced CT images and 93 texture features were extracted.The least absolute shrinkage and selection operator(LASSO)regression was used to reduct dimension of texture parameters and then the radiomics score(Rad-score)was established.The logistic regression was used to develop the prediction model with the pathological grading as the gold standard.The ROC curve and calibration curve were used to evaluate the predictive performance of the model,and the area under the curve(AUC),accuracy,sensitivity and specificity were calculated.The Hosmer-Lemeshow test was used to evaluate calibration degree of the model.Results The 10 non-zero coefficient texture features were screened out through dimension reduction steps.The Rad-score was formed according to the linear combination of these ten features and corresponding coefficients,and then the prediction model was developed.The AUC of the model in training group was 0.933(95%CI 0.862-1.000),the sensitivity was 92.3%,the specificity was 89.2%,and the model accuracy was 90.5%.The calibration curve showed the good calibration(P=0.257).The AUC value in test group was 0.875(95%CI 0.734-1.000),the sensitivity,specificity and accuracy were 72.7%,87.5%and 81.5%.The calibration curve showed the good calibration(P=0.125).Conclusion The radiomics prediction model based on unenhanced CT texture analysis have application potential for the evaluation of WHO/ISUP grading of ccRCC.