多模态超声征象结合机器学习对乳腺浸润性导管癌人表皮生长因子受体2表达的预测价值

Predictive value of multimodal ultrasound signs combined with machine learning in human epidermal growth factor receptor 2 expression in breast invasive ductal carcinoma

目的探讨多模态超声征象结合机器学习对乳腺浸润性导管癌(IDC)中人表皮生长因子受体2(HER2)表达的预测价值。方法回顾性分析华东师范大学附属芜湖医院2020年3月至2022年12月术后病理证实为乳腺IDC患者160例,所有患者均进行常规超声和自动乳腺全容积扫描(ABVS)。依据HER2的表达分为HER2阳性组和HER2阴性组。分析乳腺癌HER2阳性的影响因素;受试者操作特征曲线评估随机森林(RF)、逻辑回归(LR)、高斯朴素贝叶斯(GaussianNB)、K近邻(KNN)和支持向量机分类(SVC)机器学习对乳腺癌HER2阳性的预测价值。结果两组年龄、最大径、回声模式、形态、纵横比、导管扩张、后方回声比较,差异无统计学意义(P>0.05);两组边界、微钙化、血流分级、腋窝淋巴结肿大、冠状面特征比较,差异有统计学意义(P<0.05)。微钙化、血流分级、腋窝淋巴结肿大、冠状面特征是乳腺癌HER2阳性表达的危险因素(OR=4.077、2.608、3.093、5.734,P<0.05)。RF、LR、GaussianNB、KNN及SVC预测HER2阳性表达的曲线下面积分别是0.857、0.832、0.833、0.835、0.792。结论基于多模态超声征象结合机器学习对乳腺IDC中HER2表达具有一定的预测价值,其中RF表现最突出。

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 patients 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 characteristic curve were used to evaluate the predictive value of random forest(RF),logistic regression(LR),gaussian naive bayes(GaussianNB),K-nearest 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 expression 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 machine learning have certain predictive value for HER2 expression in breast IDC,in which RF is the most prominent.