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本文引用的文献

1
Local breast density assessment using reacquired mammographic images.使用重新获取的乳腺钼靶图像进行局部乳腺密度评估。
Eur J Radiol. 2017 Aug;93:121-127. doi: 10.1016/j.ejrad.2017.05.033. Epub 2017 May 30.
2
The epidemiology, radiology and biological characteristics of interval breast cancers in population mammography screening.人群乳腺钼靶筛查中间期乳腺癌的流行病学、放射学及生物学特征
NPJ Breast Cancer. 2017 Apr 13;3:12. doi: 10.1038/s41523-017-0014-x. eCollection 2017.
3
The effect of volumetric breast density on the risk of screen-detected and interval breast cancers: a cohort study.乳腺体积密度对筛查发现的乳腺癌和间期乳腺癌风险的影响:一项队列研究。
Breast Cancer Res. 2017 Jun 5;19(1):67. doi: 10.1186/s13058-017-0859-9.
4
Population-Attributable Risk Proportion of Clinical Risk Factors for Breast Cancer.临床乳腺癌风险因素的人群归因危险度比例。
JAMA Oncol. 2017 Sep 1;3(9):1228-1236. doi: 10.1001/jamaoncol.2016.6326.
5
Volumetric breast density affects performance of digital screening mammography.乳腺体积密度会影响数字化乳腺筛查钼靶摄影的效果。
Breast Cancer Res Treat. 2017 Feb;162(1):95-103. doi: 10.1007/s10549-016-4090-7. Epub 2016 Dec 23.
6
National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium.现代筛查数字化乳腺摄影的国家性能基准:来自乳腺癌监测联盟的更新
Radiology. 2017 Apr;283(1):49-58. doi: 10.1148/radiol.2016161174. Epub 2016 Dec 5.
7
Using Volumetric Breast Density to Quantify the Potential Masking Risk of Mammographic Density.使用乳腺体积密度来量化乳腺X线密度的潜在掩盖风险。
AJR Am J Roentgenol. 2017 Jan;208(1):222-227. doi: 10.2214/AJR.16.16489. Epub 2016 Nov 8.
8
Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS.采用半自动和全自动方法及乳腺影像报告和数据系统(BI-RADS)评估乳腺癌风险和乳腺钼靶密度
Radiology. 2017 Feb;282(2):348-355. doi: 10.1148/radiol.2016152062. Epub 2016 Sep 5.
9
Tailoring Breast Cancer Screening Intervals by Breast Density and Risk for Women Aged 50 Years or Older: Collaborative Modeling of Screening Outcomes.根据乳腺密度和风险调整50岁及以上女性的乳腺癌筛查间隔:筛查结果的协作建模
Ann Intern Med. 2016 Nov 15;165(10):700-712. doi: 10.7326/M16-0476. Epub 2016 Aug 23.
10
Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study.临床实践中放射科医生对乳腺钼靶密度评估的差异:一项多中心观察性研究。
Ann Intern Med. 2016 Oct 4;165(7):457-464. doi: 10.7326/M15-2934. Epub 2016 Jul 19.

自动化和临床乳腺成像报告和数据系统密度测量预测筛查和间期癌症的风险:病例对照研究。

Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study.

机构信息

University of California, San Francisco, San Francisco, California (K.K., A.P.M.).

Mayo Clinic College of Medicine, Rochester, Minnesota (C.G.S., S.W., M.R.J., F.F.W., K.R.B., C.M.V.).

出版信息

Ann Intern Med. 2018 Jun 5;168(11):757-765. doi: 10.7326/M17-3008. Epub 2018 May 1.

DOI:10.7326/M17-3008
PMID:29710124
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6447426/
Abstract

BACKGROUND

In 30 states, women who have had screening mammography are informed of their breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS) density categories estimated subjectively by radiologists. Variation in these clinical categories across and within radiologists has led to discussion about whether automated BI-RADS density should be reported instead.

OBJECTIVE

To determine whether breast cancer risk and detection are similar for automated and clinical BI-RADS density measures.

DESIGN

Case-control.

SETTING

San Francisco Mammography Registry and Mayo Clinic.

PARTICIPANTS

1609 women with screen-detected cancer, 351 women with interval invasive cancer, and 4409 matched control participants.

MEASUREMENTS

Automated and clinical BI-RADS density assessed on digital mammography at 2 time points from September 2006 to October 2014, interval and screen-detected breast cancer risk, and mammography sensitivity.

RESULTS

Of women whose breast density was categorized by automated BI-RADS more than 6 months to 5 years before diagnosis, those with extremely dense breasts had a 5.65-fold higher interval cancer risk (95% CI, 3.33 to 9.60) and a 1.43-fold higher screen-detected risk (CI, 1.14 to 1.79) than those with scattered fibroglandular densities. Associations of interval and screen-detected cancer with clinical BI-RADS density were similar to those with automated BI-RADS density, regardless of whether density was measured more than 6 months to less than 2 years or 2 to 5 years before diagnosis. Automated and clinical BI-RADS density measures had similar discriminatory accuracy, which was higher for interval than screen-detected cancer (c-statistics: 0.70 vs. 0.62 [P < 0.001] and 0.72 vs. 0.62 [P < 0.001], respectively). Mammography sensitivity was similar for automated and clinical BI-RADS categories: fatty, 93% versus 92%; scattered fibroglandular densities, 90% versus 90%; heterogeneously dense, 82% versus 78%; and extremely dense, 63% versus 64%, respectively.

LIMITATION

Neither automated nor clinical BI-RADS density was assessed on tomosynthesis, an emerging breast screening method.

CONCLUSION

Automated and clinical BI-RADS density similarly predict interval and screen-detected cancer risk, suggesting that either measure may be used to inform women of their breast density.

PRIMARY FUNDING SOURCE

National Cancer Institute.

摘要

背景

在 30 个州,进行过筛查性乳房 X 光检查的女性会根据放射科医生主观估计的乳房影像报告和数据系统 (BI-RADS) 密度类别了解其乳房密度。放射科医生之间以及同一放射科医生内部的这些临床分类存在差异,这引发了关于是否应该报告自动 BI-RADS 密度的讨论。

目的

确定自动 BI-RADS 密度与临床 BI-RADS 密度测量值在乳腺癌风险和检测方面是否相似。

设计

病例对照研究。

地点

旧金山乳房 X 光摄影登记处和梅奥诊所。

参与者

1609 名经筛查发现的癌症患者、351 名间隔性浸润性癌症患者和 4409 名匹配的对照参与者。

测量方法

在 2006 年 9 月至 2014 年 10 月期间,使用数字乳房 X 光摄影对自动 BI-RADS 和临床 BI-RADS 密度进行了两次评估,间隔期和筛查发现的乳腺癌风险,以及乳房 X 光摄影的敏感性。

结果

在自动 BI-RADS 分类超过 6 个月至 5 年前诊断的女性中,乳腺密度为极密的女性间隔期癌症风险高 5.65 倍(95%CI,3.33 至 9.60),筛查发现的风险高 1.43 倍(CI,1.14 至 1.79),而乳腺密度为散在纤维腺体密度的女性。间隔期和筛查发现的癌症与临床 BI-RADS 密度的关联与自动 BI-RADS 密度的关联相似,无论密度是在诊断前 6 个月至不到 2 年还是 2 至 5 年前测量的。自动和临床 BI-RADS 密度测量具有相似的判别准确性,间隔期癌症的准确性高于筛查发现的癌症(C 统计量:0.70 与 0.62[P<0.001]和 0.72 与 0.62[P<0.001])。自动和临床 BI-RADS 分类的乳房 X 光摄影敏感性相似:脂肪型,93%与 92%;散在纤维腺体密度型,90%与 90%;不均匀密度型,82%与 78%;极密型,63%与 64%。

局限性

自动和临床 BI-RADS 密度均未在新兴的乳房筛查方法断层合成术上进行评估。

结论

自动和临床 BI-RADS 密度可相似地预测间隔期和筛查发现的癌症风险,这表明可以使用任何一种测量方法来告知女性其乳房密度。

主要资金来源

美国国家癌症研究所。