基于临床信息的logistic回归模型在乳腺影像报告和数据系统4类中对病灶良恶性的鉴别价值  

Logistic regression models based on clinical information in discriminating breast malignant lesions from benign lesions of Breast Imaging Reporting and Data System 4

作　　者：林晓佳 马乐[1] 蔡裕兴[1] 陈卫国[1] LIN Xiaojia;MA Le;CAI Yuxing;CHEN Weiguo(Department of Radiology,Nanfang Hospital,Southern Medical University,Guangzhou 510515,Guangdong Province,China)

机构地区：[1]南方医科大学南方医院放射科,广东广州510515

出　　处：《肿瘤影像学》2020年第2期85-89,共5页Oncoradiology

基　　金：广东省自然科学基金(2019A1515011168);广东省医学科学技术研究基金(B2018017)。

摘　　要：目的:探讨基于患者临床信息的logistic回归模型在乳腺影像报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)4类中鉴别病灶良恶性的价值。方法:回顾并收集经过病理学检查证实的BI-RADS4类乳腺病灶患者221例(良性133例,恶性88例)的临床信息。采用logistic回归分析筛选能够鉴别病灶良恶性的临床信息特征,建立回归模型。比较BI-RADS联合模型与单独采用BI-RADS分类在鉴别乳腺良恶性病灶上的区别。结果:经logistic回归分析,发现9个临床信息特征与乳腺良恶性病灶相关,其中是否触及病灶(OR=7.196)、病灶是否固定(OR=10.150)、病灶最大径是否>2 cm(OR=4.208)等3个特征有较高的危险度(P<0.05)。单独采用BI-RADS分类,其诊断灵敏度为86.3%、特异度为69.9%、准确率为76.5%;将BI-RADS分类联合回归模型,其灵敏度为88.6%、特异度为73.7%、准确率为79.6%。结论:BI-RADS分类联合基于患者临床信息的logistic回归模型有助于提高鉴别乳腺病灶良恶性的诊断效能,减少不必要的良性活检。Objective:To explore the value of logistic regression model based on subjects’clinical information in discriminating breast malignant lesions from benign lesions of Breast Imaging Reporting and Data System(BI-RADS)4.Methods:Retrospectively 221 subjects(133 benign and 88 malignant)confirmed by histopathology were recruited whose BI-RADS grade was 4 and the clinical information were collected.Logistic regression analysis was used to screen the clinical information features that can discriminate malignant from benign lesions and a regression model was established.The comparison was made between regression model combined with BI-RADS and BI-RADS classification alone for differential diagnosis between malignant and benign lesions.Results:Nine clinical information features were found to be related to malignant and benign lesions.Three features of whether the lesion can be touched(OR=7.196),whether the lesion position was fixed(OR=10.150),and whether the maximum diameter of the lesion was more than 2 cm(OR=4.208)have a higher risk than other clinical information(P<0.05).Using BI-RADS classification alone,the diagnostic sensitivity,specificity and accuracy were 86.3%,69.9%and 76.5%;the diagnostic sensitivity,specificity and accuracy of regression model combined with BI-RADS were 88.6%,73.7%and 79.6%.Conclusion:Logistic regression model based on subjects’clinical information combined with BI-RADS classification is helpful to improve the diagnostic efficiency of malignant and benign lesions and further to reduce unnecessary benign biopsy.

关 键 词：乳腺病灶 良恶性 LOGISTIC分析 鉴别模型 

分 类 号：R737.9[医药卫生—肿瘤] R445.4[医药卫生—临床医学]