Literature DB >> 20966340

Features of prospectively overlooked computer-aided detection marks on prior screening digital mammograms in women with breast cancer.

Nariya Cho1, Seung Ja Kim, Hye Young Choi, Chae Yeon Lyou, Woo Kyung Moon.   

Abstract

OBJECTIVE: The purpose of this article is to describe the features of prospectively overlooked computer-aided detection (CAD) marks on prior screening digital mammograms for women with breast cancer. SUBJECTS AND METHODS: A CAD system embedded in a digital mammography system was prospectively applied to 50,100 screening mammograms between December 2003 and December 2006. Each mammogram was originally interpreted by one of five radiologists using the CAD information. Seventy-five mammogram pairs of prior negative screening mammograms and subsequent mammograms of developed cancers were collected. Visible findings and their actionability were determined by three blinded radiologists. All CAD marks, both true-positive and false-positive, and the number of marked views for the visible findings on prior mammograms were analyzed.
RESULTS: Of the 75 areas where cancer later developed, 61% (46/75) of mammograms had visible findings (21 masses, 17 microcalcifications, and eight masses with microcalcifications). Of these visible findings, 46% (21/46) were determined to be actionable, and 54% (25/46) were underthreshold. The CAD system had correctly depicted 74% (34/46) of the visible findings-52% (11/21) of masses, 94% (16/17) of microcalcifications, and 88% (7/8) of masses with microcalcifications. Actionable findings showed higher CAD sensitivity than did underthreshold findings (90% [19/21] vs 60% [15/25]; p = 0.04) and were more often marked on both views (58% [11/19] vs 27% [4/15]; p = 0.09). The average number of false-positive marks per case was 1.61.
CONCLUSION: On prior screening digital mammograms, the CAD system had correctly marked 74% (34/46) of visible findings and 90% (19/21) of actionable findings. The actionable findings showed significantly higher CAD sensitivity and were marked on both mammographic views more often than the underthreshold findings were.

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Year:  2010        PMID: 20966340     DOI: 10.2214/AJR.10.4494

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  1 in total

1.  Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features.

Authors:  Shulong Li; Panpan Xu; Bin Li; Liyuan Chen; Zhiguo Zhou; Hongxia Hao; Yingying Duan; Michael Folkert; Jianhua Ma; Shiying Huang; Steve Jiang; Jing Wang
Journal:  Phys Med Biol       Date:  2019-09-04       Impact factor: 3.609

  1 in total

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