Literature DB >> 11274556

Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection.

R L Birdwell1, D M Ikeda, K F O'Shaughnessy, E A Sickles.   

Abstract

PURPOSE: To retrospectively determine the mammographic characteristics of cancers missed at screening mammography and assess the ability of computer-aided detection (CAD) to mark the missed cancers.
MATERIALS AND METHODS: A multicenter retrospective study accrued 1,083 consecutive cases of breast cancer detected at screening mammography. Prior mammograms were available in 427 cases. Of these, 286 had lesions visible in retrospect. The 286 cases underwent blinded review by panels of radiologists; a majority recommended recall for 112 cases. Two experienced radiologists compared prior mammograms in 110 of these cases with the subsequent screening mammograms (when cancer was detected), noting mammographic characteristics of breast density, lesion type, size, morphology, and subjective reasons for possible miss. The prior mammograms were then analyzed with a CAD program.
RESULTS: There were 110 patients with 115 cancers. On the prior mammograms with missed cancers, 35 (30%) of the 115 lesions were calcifications, with 17 of 35 (49%) clustered or pleomorphic. Eighty of the 115 (70%) were mass lesions, with 32 of 80 (40%) spiculated or irregular. For calcifications and masses, the most frequently suggested reasons for possible miss were dense breasts (12 of 35; 34%) and distracting lesions (35 of 80; 44%), respectively. CAD marked 30 (86%) of 35 missed calcifications and 58 (73%) of 80 missed masses.
CONCLUSION: Detection errors affected cases with calcifications and masses. CAD marked most (77%; 88 of 115) cancers missed at screening mammography that radiologists retrospectively judged to merit recall.

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Mesh:

Year:  2001        PMID: 11274556     DOI: 10.1148/radiology.219.1.r01ap16192

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


  73 in total

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Authors:  Bradley M Hemminger
Journal:  J Digit Imaging       Date:  2003-12-15       Impact factor: 4.056

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3.  Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms.

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4.  Detection of architectural distortion in prior mammograms via analysis of oriented patterns.

Authors:  Rangaraj M Rangayyan; Shantanu Banik; J E Leo Desautels
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Review 5.  [Clinical results of digital mammography].

Authors:  R Schulz-Wendtland; K-P Hermann; W Bautz
Journal:  Radiologe       Date:  2005-03       Impact factor: 0.635

6.  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

7.  Evaluating the effect of a wavelet enhancement method in characterization of simulated lesions embedded in dense breast parenchyma.

Authors:  L Costaridou; S Skiadopoulos; P Sakellaropoulos; E Likaki; C P Kalogeropoulou; G Panayiotakis
Journal:  Eur Radiol       Date:  2005-02-09       Impact factor: 5.315

8.  Impact of breast density on computer-aided detection in full-field digital mammography.

Authors:  Silvia Obenauer; Christian Sohns; Carola Werner; Eckhardt Grabbe
Journal:  J Digit Imaging       Date:  2006-09       Impact factor: 4.056

9.  Usefulness of texture analysis for computerized classification of breast lesions on mammograms.

Authors:  Roberto R Pereira; Paulo M Azevedo Marques; Marcelo O Honda; Sergio K Kinoshita; Roger Engelmann; Chisako Muramatsu; Kunio Doi
Journal:  J Digit Imaging       Date:  2007-09       Impact factor: 4.056

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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