摘要
目的:探讨乳腺X线Quantra软件与乳腺影像报告和数据系统(Breast Imaging Reporting and Data Systern,BIRADS)分类评价乳腺密度(mammographic density,MD)的一致性。方法:回顾并分析2016年1—11月于天津医科大学肿瘤医院行乳腺X线摄影检查的2550例患者的影像学资料,原始图像采用Quantra 2.0软件进行分析,自动计算出MD、q_abd及Q_abd。依据BI-RADS分类,由2名从事乳腺影像诊断的高年资放射医师独立阅片并评价MD。采用SPSS 19.0软件对乳腺X线Quantra软件Q_abd值与BI-RADS分类进行一致性检验(Kappa检验)。利用受试者工作特征(receiver operating characteristic,ROC)曲线分析Quantra软件Q_abd值与BI-RADS 2分类(致密型与非致密型)的关系,其曲线下面积(area under curve,AUC)、95%CI、灵敏度、特异度和界值。结果:BI-RADS 4分类与Quantra软件测得的Q_abd值进行一致性分析,Kappa值为0.665,而2分类时Kappa值为0.760。利用ROC曲线分析Quantra软件Q_abd值与BI-RADS 2分类的关系,其AUC为0.957、95%CI:0.951~0.964、灵敏度为86.0%、特异度为99.2%,致密型乳腺与非致密型乳腺的界值为13%。结论:乳腺X线摄影采用Quantra软件评价MD是一种较为简单、客观和准确的方法,并能有效避免较多依赖于放射医师经验的主观判断。
Objective:To discuss the mammographic density(MD)assessment by Quantra software as compared to Breast Imaging Reporting and Data Systems(BI-RADS).Methods:Retrospective analysis of 4028 cases of female patients with mammography at Tianjin Medical University Cancer Institute and Hospital was performed.Images of mammography were analyzed with Quantra software,the computer automatically calculated the MD,q_abd value and Q_abd value.Two experienced radiologists read the mammograms and assessed the MD with BI-RADS classification.Statistical analysis was carried out with SPSS 19.0 soffware by using Kappa test for analyzing the consistency between Q_abd value and BI-RADS.To calculate the consistency analysis between the MD measured by Quantra software and BI-RADS.Receiver operating characteristic(ROC)curve was used to analyze between Q_abd value and BI-RADS(two-grade scale)with the area under curve(AUC),95%CI,sensitivity,specificity and limit value.Results:Agreement(weighted Kappa)between BI-RADS and Quantra was 0.665 and 0.760 on four-and two-grade scales,respectively.The AUC,95%CI,sensitivity,specificity and cut-off value was 0.957,0.951-0.964,86.0%,99.2%and 13%,respectively.Conclusion:Quantra software is a relatively simple,objective and accurate method to evaluate MD,and can effectively avoid more dependent on subjective judgment of the radiologist.
作者
张连连
柳杰
路红
马文娟
刘佩芳
ZHANG Lianlian;LIU Jie;LU Hong;MA Wenjuan;LIU Peifang(Department of Breast Imaging,Tianjin Medical University Cancer Institute and Hospital,National Clinical Research Center for Cancer,Tianjin's Key Laboratory of Cancer Prevention and Therapy,Tianjin’s Clinical Research Center for Cancer,Key Laboratory of Breast Cancer Prevention and Therapy,Tianjin Medical University,Ministry of Education,Tianjin 300060,China)
出处
《肿瘤影像学》
2020年第3期272-276,共5页
Oncoradiology
基金
国家自然科学基金(81801781)。
