To evaluate the potential application of computed tomography (CT) radiomics in the prediction of BRCA1-associated protein 1 () mutation status in patients with clear-cell renal cell carcinoma (ccRCC). In this retrospective study, clinical and CT imaging data of 54 patients were retrieved from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma database. Among these, 45 patients had wild-type and nine patients had mutation. The texture features of tumor images were extracted using the Matlab-based IBEX package. To produce class-balanced data and improve the stability of prediction, we performed data augmentation for the mutation group during cross validation. A model to predict mutation status was constructed using Random Forest Classification algorithms, and was evaluated using leave-one-out-cross-validation. Random Forest model of predict mutation status had an accuracy of 0.83, sensitivity of 0.72, specificity of 0.87, precision of 0.65, AUC of 0.77, F-score of 0.68. CT radiomics is a potential and feasible method for predicting mutation status in patients with ccRCC.Copyright © 2020 Feng, Zhang, Qi, Shen, Hu and Chen.
February 18, 2021
Contemporary rates and predictors of open conversion during minimally invasive partial nephrectomy for kidney cancer.
January 7, 2021
October 8, 2020
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