Objective To investigate the predictive value of machine learning combined with ultrasonography in breast cancer susceptibility gene(BRCA)mutations in patients with epithelial ovarian carcinoma(EOC).Methods The imag-ing and pathological data of 105 patients with EOC who underwent ultrasound examination in Sichuan Mianyang 404 Hospital from February 2022 to June 2023 were collected retrospectively.The patients were divided into model group(n=70)and veri-fication group(n=35).The patients in the model group were divided into mutation group(n=22)and wild group(n=48)ac-cording to the mutation of BRCA gene.The clinical data and imaging features of the two groups were compared.According to clinical data and imaging characteristics,clinical(Clinical)model,imaging(Rad)model and combined(Combine)model were established to predict BRCA gene mutation in patients with EOC.The predictive value of the three models was evaluated by receiver operating characteristic(ROC)curve,calibration curve and clinical decision curve.Results Multivariate Logistic regression analysis showed that gender(OR=1.754,95％CI:1.573-1.942),smoking history(OR=1.611,95％CI:1.523-1.822)and family history(OR=3.554,95％CI:1.324-5.684)were independent risk factors for BRCA gene mutation in EOC patients.According to the LASSO regression method,the features were downscaling,and the optimal feature subset was selected by ten-fold cross-validation,and a total of 7 imaging features with less correlation were selected.The ROC curve of the modeling group and the verification group showed that the three models had good predictive efficiency.Delong test showed that the area under the ROC curve of the Combine model in the modeling group and verification group was significantly higher than that in Rad and Clinical(both P＜0.05).The calibration curve shows that each model has a good fitting effect in the modeling group and verification group.The clinical decision curve shows that the Combine model has higher clinical practical value than the other two models in the modeling group and verification group.Conclusion The combination of machine learning and ultra-sound imaging has a good predictive value for BRCA mutation in patients with EOC.
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