关键词:

机器学习, 影像组学, 肾透明细胞癌

Abstract:

Objective Using the enhanced CT radiomics in differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma. Methods Retrospective analysis was performed on 28 cases of homogeneous clear cell renal cell carcinoma and 22 cases of renal angiomyolipoma without visible fat， which were confirmed by postoperative pathology. The arterial and venous phase images manual sketch tumor area were concerned，About 1 706 radiomics features，data after normalization and by space dimension reduction，were extracted from the image. Features were screened to establish LR，SVM，RF model respectively，five ⁃fold cross validation and selecting cross validation were used to set the highest AUC model as the best model. The clinical features were analyzed to determine the predictors and the clinical model. The comprehensive model was established by multi⁃factor binary Logistic regression based on the predicted values of the selected optimal radiomics model and clinical predictors. The nomogram based on combined with clinical factor and radiomics model. The test of Hosmer⁃ Lemeshow was used to evaluate the fitness of the line chart. Decision curve analysis was applied for clinical use. Results The best radiomics model was LR model，and the AUC of the model verified by Bootstrap method was 0.836（95%CI：0.701～0.927）. The AUC value of the comprehensive model was 0.869（95%CI：0.701～0.927），and the calibration curve of the nomogram showed good consistency. Decision curve analysis verified the clinical usefulness of the predictive nomogram. Conclusion The combination of clinical factors and radiomics features has a strong ability to distinguish renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma 

Key words:

machine learning, radiomics, clear cell renal cell carcinoma