Literature DB >> 15229350

Can computer-aided detection with double reading of screening mammograms help decrease the false-negative rate? Initial experience.

Stamatia V Destounis1, Patricia DiNitto, Wende Logan-Young, Ermelinda Bonaccio, Margarita L Zuley, Kathleen M Willison.   

Abstract

PURPOSE: To retrospectively evaluate the role of computer-aided detection (CAD) in reducing the rate of false-negative (FN) findings on screening mammograms considered normal at initial double reading.
MATERIALS AND METHODS: At the authors' institution, independent prospective double readings in which the second reader is not blinded to results of the first reading are performed routinely for all mammograms. When cancer is diagnosed, prior mammograms also are reviewed with double reading to determine cancer visibility. Findings are categorized as (a) no evidence of cancer on any prior screening mammogram and patient presents more than 1 year after prior screening, (b) no evidence of cancer on any prior screening mammogram and patient presents with symptoms within 1 year after prior screening (year-interval occult false-negative), or (c) cancer visible. The clinical director separately evaluates each case in the same way. In 2000, 519 histologically proved breast cancers were diagnosed, including 132 for which patients sought a second opinion and FN findings were not tracked. Prior screening mammograms were available in 318 of the other 387 cases. Five radiologists in two reading sessions independently reviewed current and prior mammograms to categorize visible cancers as either threshold or actionable FN findings. Visible cancers deemed actionable by at least three of five readers were analyzed with a commercially available CAD system. FN rates were calculated prior to and after CAD analysis.
RESULTS: Twenty-seven occult and 71 visible cancers were found (total FN findings, 98). Three of five readers considered 52 (73%) of 71 visible cancers actionable. The CAD system correctly marked 37 (71%) of these 52 on prior screening mammograms (19 [65%] of 29 masses, seven [88%] of eight microcalcifications, seven [78%] of nine architectural distortions, and four [67%] of six masses with microcalcifications). The FN rate was 98 (31%) of 318 before CAD and 61 (19%) of 318 after CAD.
CONCLUSION: In this retrospective review of this small subset of cancers, it appears that CAD has the potential to decrease the FN rate at double reading by more than one-third (from 31% to 19%). The CAD system correctly marked 37 (71%) of 52 actionable findings read as negative in previous screening years. Copyright RSNA, 2004

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Year:  2004        PMID: 15229350     DOI: 10.1148/radiol.2322030034

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  26 in total

1.  Computer-aided detection of breast masses on full field digital mammograms.

Authors:  Jun Wei; Berkman Sahiner; Lubomir M Hadjiiski; Heang-Ping Chan; Nicholas Petrick; Mark A Helvie; Marilyn A Roubidoux; Jun Ge; Chuan Zhou
Journal:  Med Phys       Date:  2005-09       Impact factor: 4.071

Review 2.  CAD for mammography: the technique, results, current role and further developments.

Authors:  Ansgar Malich; Dorothee R Fischer; Joachim Böttcher
Journal:  Eur Radiol       Date:  2006-01-17       Impact factor: 5.315

3.  Computer-aided detection system for clustered microcalcifications: comparison of performance on full-field digital mammograms and digitized screen-film mammograms.

Authors:  Jun Ge; Lubomir M Hadjiiski; Berkman Sahiner; Jun Wei; Mark A Helvie; Chuan Zhou; Heang-Ping Chan
Journal:  Phys Med Biol       Date:  2007-01-23       Impact factor: 3.609

4.  Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

Authors:  Jonathan L Jesneck; Loren W Nolte; Jay A Baker; Carey E Floyd; Joseph Y Lo
Journal:  Med Phys       Date:  2006-08       Impact factor: 4.071

5.  Bilateral analysis based false positive reduction for computer-aided mass detection.

Authors:  Yi-Ta Wu; Jun Wei; Lubomir M Hadjiiski; Berkman Sahiner; Chuan Zhou; Jun Ge; Jiazheng Shi; Yiheng Zhang; Heang-Ping Chan
Journal:  Med Phys       Date:  2007-08       Impact factor: 4.071

Review 6.  Computer-aided diagnosis in medical imaging: historical review, current status and future potential.

Authors:  Kunio Doi
Journal:  Comput Med Imaging Graph       Date:  2007-03-08       Impact factor: 4.790

Review 7.  Incorporating new imaging models in breast cancer management.

Authors:  Denise H Reddy; Ellen B Mendelson
Journal:  Curr Treat Options Oncol       Date:  2005-03

8.  False positive marks on unsuspicious screening mammography with computer-aided detection.

Authors:  Mary C Mahoney; Karthikeyan Meganathan
Journal:  J Digit Imaging       Date:  2011-10       Impact factor: 4.056

9.  Dynamic multiple thresholding breast boundary detection algorithm for mammograms.

Authors:  Yi-Ta Wu; Chuan Zhou; Heang-Ping Chan; Chintana Paramagul; Lubomir M Hadjiiski; Caroline Plowden Daly; Julie A Douglas; Yiheng Zhang; Berkman Sahiner; Jiazheng Shi; Jun Wei
Journal:  Med Phys       Date:  2010-01       Impact factor: 4.071

Review 10.  Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.

Authors:  Krzysztof J Geras; Ritse M Mann; Linda Moy
Journal:  Radiology       Date:  2019-09-24       Impact factor: 11.105

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