Abstract：Objective To investigate the relationship between Von Hippel-Lindau (VHL) gene expression and Fuhrman grade of clear cell carcinoma of kidney (ccRCC), and to construct a diagnostic model for predicting Fuhrman grade of ccRCC based on CT
omics characteristics matching VHL expression. Methods All the cases in this study were from TCGA database, including 163
pathologically confirmed cases of ccRCC. Tumor mass volume (TMV) was constructed on CT images of the enrolled cases. A total
of 588 imaging features were calculated in each ccRCC cancer focus. Cases were divided into the training group (n=120) and the
test group (n=43). In the training group, rank correlation analysis was used to select CT histological features that were statistically
different from ccRCC Fuhrman grading (P<0.01). The ccRCC Fuhrman grade was tested by receiver operator characteristic (ROC)
diagnostic efficiency using the selected omics features with statistical difference. A diagnostic model consisting of omics features and
learning classifiers was constructed. In the test group, the ccRCC Fuhrman grade was tested by ROC diagnostic efficiency using the
statistically significant omics features screened out by the training group, and the diagnostic model of effective CT omics features to
predict ccRCC Fuhrman grade was constructed. Results The distribution of VHL expression in ccRCC Fuhrman grade groups was
statistically different (P=0.037). Through correlation analysis, 25 CT omics features with statistical differences were screened out in
the training group, which had high diagnostic efficacy for ccRCC Fuhrman classification, with AUC value of 0.742 (0.654~0.817),
sensitivity of 79.0%, specificity of 61.4%. The construct predictive diagnostic model was built. In the test group, to verify the
diagnostic efficacy of the model, the AUC value of 25 CT omics features for high-low grade Fuhrman diagnosis of ccRCC was 0.816,
95%CI: 0.668~0.918, sensitivity 90.9%, specificity 61.9%. Conclusion The CT omics model related to VHL gene mutation has high
diagnostic efficacy for Fuhrman classification prediction of ccRCC.
田晋捷，马新伟，许建铭，庞洪权，朱建兵. 基于VHL基因表达构建CT组学特征预测ccRCC Fuhrman分级的诊断模型[J]. 中国医疗设备, 2022, 37(8): 118-124.
TIAN Jinjie, MA Xinwei, XU Jianming, PANG Hongquan, ZHU Jianbing. CT Omics Based on Von Hippel-Lindau Gene Expression to Predict the Diagnosis of Fuhrman
Grading of ccRCC. China Medical Devices, 2022, 37(8): 118-124.
