Noble Meredith, Bruening Wendy, Uhl Stacey, Schoelles Karen
ECRI Institute, Evidence-based Practice Center, Plymouth Meeting, 19462, USA.
Arch Gynecol Obstet. 2009 Jun;279(6):881-90. doi: 10.1007/s00404-008-0841-y. Epub 2008 Nov 21.
Mammography is generally accepted as the best available breast cancer screening method; however, some cancers detectable on mammography images are missed. Computer-aided detection (CAD) systems for mammography are intended to reduce false negatives by marking suspicious areas of the mammograms for reviewers to consider. Although the prospect of improving the sensitivity of screening mammograms has led to the diffusion of CAD for mammography, little is known about its diagnostic accuracy.
To assess the diagnostic performance of CAD for screening mammography in terms of sensitivity and specificity and incremental recall, biopsy, and cancer diagnosis rates.
Published literature identified by systematic literature searches of 17 databases, including MEDLINE, EMBASE, and the Cochrane Library, searched through 25 September 2008.
A reviewer and an information specialist selected full-length English-language articles that enrolled asymptomatic women for routine breast cancer screening and provided data needed for our analyses using criteria established a priori. We identified 75 potentially relevant publications, of which 7 (9%) were included.
Data were extracted and internal validity was assessed by a single review author, and forms were approved by the co-authors.
Three studies (n = 347,324) reported sensitivity and specificity, or data to calculate them, and five studies (n = 51,162) reported data to calculate incremental rates of cancer diagnoses and recall and biopsy of women who did not have breast cancer. The pooled sensitivity was 86.0% (95% CI 84.2-87.6%) and specificity was 88.2% (95% CI 88.1-88.3%). Of the 100,000 women screened, CAD yielded an additional 50 (95% CI 30-80) correct breast cancer diagnoses, 1,190 (95% CI 1,090-1,290) recalls of healthy women, and 80 (95% CI 60-100) biopsies of healthy women. A total of 96% (95% CI 93.9-97.3%) of women recalled based upon CAD and 65.1% (95% CI 52.3-76.0%) of women biopsied based upon CAD were healthy. No studies reported patient-oriented clinical outcomes.
乳腺钼靶摄影通常被认为是现有的最佳乳腺癌筛查方法;然而,钼靶摄影图像上可检测到的一些癌症却被漏诊。乳腺钼靶摄影的计算机辅助检测(CAD)系统旨在通过标记钼靶图像上的可疑区域供阅片者参考,以减少假阴性。尽管提高筛查钼靶摄影敏感性的前景促使CAD在乳腺钼靶摄影中得到推广,但对其诊断准确性却知之甚少。
从敏感性、特异性以及增加的召回率、活检率和癌症诊断率方面评估CAD用于筛查乳腺钼靶摄影的诊断性能。
通过对17个数据库(包括MEDLINE、EMBASE和Cochrane图书馆)进行系统文献检索确定的已发表文献,检索截至2008年9月25日。
一名审阅者和一名信息专家根据事先确定的标准,选择纳入无症状女性进行常规乳腺癌筛查并提供我们分析所需数据的全文英文文章。我们确定了75篇潜在相关出版物,其中7篇(9%)被纳入。
数据由一名审阅作者提取并评估内部有效性,表格经共同作者批准。
三项研究(n = 347,324)报告了敏感性和特异性或计算它们的数据,五项研究(n = 51,162)报告了计算未患乳腺癌女性的癌症诊断增加率以及召回率和活检率的数据。合并敏感性为86.0%(95%CI 84.2 - 87.6%),特异性为88.2%(95%CI 88.1 - 88.3%)。在100,000名接受筛查的女性中,CAD额外做出了50例(95%CI 30 - 80)正确的乳腺癌诊断,召回了1,190名(95%CI 1,090 - 1,290)健康女性,对80名(95%CI 60 - 100)健康女性进行了活检。基于CAD召回的女性中,共有96%(95%CI 93.9 - 97.3%)为健康女性,基于CAD进行活检的女性中,65.1%(95%CI 52.3 - 76.0%)为健康女性。没有研究报告以患者为导向的临床结局。