Objective To observe the value of venous phase CT radiomics combined with clinical features for predicting breast cancer susceptibility gene(BRCA)mutation in patients with epithelial ovarian cancer(EOC).Methods A total of 111 EOC patients diagnosed by surgical pathology and BRCA detection were retrospectively enrolled and divided into training set(n=90,35 BRCA mutations[+]and 55 BRCA mutations[-])and test set(n=21,8 BRCA mutations[+]and 13 BRCA mutations[-])at the ratio of 8∶2.Clinical and CT data were analyzed using univariate and multivariate logistic regression(LR)to screen independent predictors of BRCA mutations in EOC patients,and then a clinical model was established.Based on venous phase CT,the best radiomics features of EOC lesions were extracted and screened,radiomics score(Radscore)was calculated.Machine learning(ML)models were established using random forest(RF),support vector machine(SVM)and LR,respectively,and the optimal ML model was screened.Finally a combined model was constructed based on Radscore and independent predictors.The predictive efficacy and clinical value of each model were evaluated.Results Human epididymis protein 4 was the independent predictor of BRCA mutation in EOC patients,and the area under the curve(AUC)of clinical model was 0.648 and 0.742 in training and test sets,respectively.AUC of RF,SVM and LR model was 0.726,0.763 and 0.860 in training set,0.708,0.750 and 0.700 in test set,respectively,and SVM model was the optimal ML model.AUC of combined model was 0.819 and 0.783 in training and test set,respectively,which in training set was higher than that of clinical model(P=0.022).No significant difference of AUC was found by pairwise comparison of other models in both training and test set(all P＞0.05).Decision curve analysis showed that when the threshold was larger than 0.15,the clinical value of combined model was higher than that of clinical and SVM models.Conclusion Venous phase CT radiomics combined with clinical features could effectively predict BRCA mutation in EOC patients.

Objective To observe the value of venous phase CT radiomics combined with clinical features for predicting breast cancer susceptibility gene(BRCA)mutation in patients with epithelial ovarian cancer(EOC).Methods A total of 111 EOC patients diagnosed by surgical pathology and BRCA detection were retrospectively enrolled and divided into training set(n=90,35 BRCA mutations[+]and 55 BRCA mutations[-])and test set(n=21,8 BRCA mutations[+]and 13 BRCA mutations[-])at the ratio of 8∶2.Clinical and CT data were analyzed using univariate and multivariate logistic regression(LR)to screen independent predictors of BRCA mutations in EOC patients,and then a clinical model was established.Based on venous phase CT,the best radiomics features of EOC lesions were extracted and screened,radiomics score(Radscore)was calculated.Machine learning(ML)models were established using random forest(RF),support vector machine(SVM)and LR,respectively,and the optimal ML model was screened.Finally a combined model was constructed based on Radscore and independent predictors.The predictive efficacy and clinical value of each model were evaluated.Results Human epididymis protein 4 was the independent predictor of BRCA mutation in EOC patients,and the area under the curve(AUC)of clinical model was 0.648 and 0.742 in training and test sets,respectively.AUC of RF,SVM and LR model was 0.726,0.763 and 0.860 in training set,0.708,0.750 and 0.700 in test set,respectively,and SVM model was the optimal ML model.AUC of combined model was 0.819 and 0.783 in training and test set,respectively,which in training set was higher than that of clinical model(P=0.022).No significant difference of AUC was found by pairwise comparison of other models in both training and test set(all P＞0.05).Decision curve analysis showed that when the threshold was larger than 0.15,the clinical value of combined model was higher than that of clinical and SVM models.Conclusion Venous phase CT radiomics combined with clinical features could effectively predict BRCA mutation in EOC patients.