Predictive value of multimodal ultrasound signs combined with machine learning in human epidermal growth factor receptor 2 expression in breast invasive ductal carcinoma
Objective To explore the predictive value of multimodal ultrasound signs combined with machine learning in human epidermal growth factor receptor 2(HER2)expression in breast invasive ductal carcinoma(IDC).Methods A retrospective analysis was performed on 160 patients with breast IDC confirmed by pathology after surgery in Wuhu Hospital,East China Normal University from March 2020 to December 2022,all pa-tients underwent routine ultrasound and automated breast volume scanner(ABVS).According to the HER2 expression,they were divided into HER2-positive group and HER2-negative group.The influencing factors of HER2 positive breast cancer were analyzed;receiver operating charac-teristic curve were used to evaluate the predictive value of random forest(RF),logistic regression(LR),gaussian naive bayes(GaussianNB),K-near-est neighbor(KNN),and support vector classification(SVC)machine learning for HER2 positive breast cancer.Results There were no significant differences in age,maximum diameter,echo pattern,shape,aspect ratio,catheter dilation,and rear echo between two groups(P＞0.05);there were significant differences in boundary,microcalcification,blood flow grade,axillary lymph node enlargement,and coronal features between two groups(P＜0.05).Microcalcification,blood flow grade,axillary lymph node enlargement,and coronal features were risk factors for HER2 positive expres-sion in breast cancer(OR=4.077,2.608,3.093,5.734,P＜0.05).The area under the curve of RF,LR,GaussianNB,KNN,and SVC predicted HER2 positive expression were 0.857,0.832,0.833,0.835,and 0.792,respectively.Conclusion Multimodal ultrasound signs combined with ma-chine learning have certain predictive value for HER2 expression in breast IDC,in which RF is the most prominent.
Breast cancerUltrasound examinationHuman epidermal growth factor receptor 2Automated breast volume scanner
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