Prediction of BRCA Gene Mutation Status in Epithelial Ovarian Cancer by Radiomics Models based on 2D and 3D CT Images

Abstract

Background The objective of this study is to explore the value of two-dimensional (2D) and three-dimensional (3D) radiomics models based on enhanced computed tomography(CT) images in predicting BRCA gene mutations in patients with epithelial ovarian cancer. Methods A retrospective analysis of the clinical and imaging data of 122 patients with ovarian cancer confirmed by surgery and pathology and on which genetic testing was performed. Radiomics features were extracted from the 2D and 3D regions of interest of the patients’ primary tumor lesions, and features were selected in the training set using the maximum correlation and minimum redundancy method. Then, the best features were selected through Lasso 10-fold cross-validation. Feature subsets were used to establish a radiomics model. We used area under the receiver operating characteristic curve analysis to evaluate the model’s performance and then used the model’s decision curve to evaluate its clinical validity. Results On the validation set, the area under the curve values of the 2D, 3D, and 2D+3D combined models was 0.78 (0.61–0.96), 0.75 (0.55–0.92), and 0.82 (0.61–0.96), respectively. However, the DeLong test P values between the three pairs of models were all >0.05. The decision curve analysis showed that the radiomics model had a high net benefit across all high-risk threshold probabilities. Conclusions The three radiomics models can predict the BRCA gene mutation in ovarian cancer, and there were no statistically significant differences between the three models’ prediction performance.