Literature DB >> 12623042

Clinical performance of computer-assisted detection (CAD system in detecting carcinoma in breasts of different densities.

W T Ho1, P W T Lam.   

Abstract

OBJECTIVES: To determine the clinical performance of a computer-assisted detection (CAD) system in detecting carcinoma in breasts of different densities.
MATERIALS AND METHODS: A total of 264 sets of bilateral screening mammograms taken in craniocaudal and medial-lateral oblique projections during the year 1997 were divided into four groups according to the BI-RADS density classification: fatty (pattern 1), scattered fibroglandular (pattern 2), heterogeneously dense (pattern 3) and extremely dense (pattern 4). Each group contained about 60% normal and 40% biopsy-proven cancer cases. Of the malignant cases, there were a mixture of mammographic findings including focal masses (<2.5 cm), asymmetrical density, architectural distortion or microcalcifications. Films with artefacts and obvious masses>2.5 cm were not included. The chosen cases were then digitized and analysed by the CAD system. Sensitivity was calculated as detection of cancer by at least one marker in at least one view. Specificity was calculated as the number of false-positive marks per image on normal cases. Statistical tests of significance were performed by using contingency tables and Chi square test.
RESULTS: The CAD system detected 14 out of the total 15 cancer cases in totally fatty breasts with a sensitivity of 93.3% at a specificity of 1.3 false-positive marks per image. In breasts with scattered fibroglandular pattern, the sensitivity was 93.9% (31/33) and the specificity was 1.6 false-positive marks per image while in heterogeneously dense breasts, the sensitivity of the CAD system fell to 84.8% at a specificity of 1.6 false-positive marks per image. The sensitivity of the CAD system further dropped to 64.3% in markedly dense breasts while maintaining a specificity of 1.2 false-positive marks per image. The decrease in sensitivity in dense breast was found to be significant (p=0.046).
CONCLUSION: The sensitivity of the CAD system deteriorated significantly as the density of the breast increased while the specificity of the system remained relatively constant.

Mesh:

Year:  2003        PMID: 12623042     DOI: 10.1053/crad.2002.1131

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  16 in total

1.  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

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.  A statistical approach for breast density segmentation.

Authors:  Arnau Oliver; Xavier Lladó; Elsa Pérez; Josep Pont; Erika R E Denton; Jordi Freixenet; Joan Martí
Journal:  J Digit Imaging       Date:  2009-06-09       Impact factor: 4.056

4.  Evaluation of breast amorphous calcifications by a computer-aided detection system in full-field digital mammography.

Authors:  A M Scaranelo; R Eiada; K Bukhanov; P Crystal
Journal:  Br J Radiol       Date:  2012-05       Impact factor: 3.039

5.  Breast Density Analysis Using an Automatic Density Segmentation Algorithm.

Authors:  Arnau Oliver; Meritxell Tortajada; Xavier Lladó; Jordi Freixenet; Sergi Ganau; Lidia Tortajada; Mariona Vilagran; Melcior Sentís; Robert Martí
Journal:  J Digit Imaging       Date:  2015-10       Impact factor: 4.056

6.  Mammographic image denoising and enhancement using the Anscombe transformation, adaptive wiener filtering, and the modulation transfer function.

Authors:  Larissa C S Romualdo; Marcelo A C Vieira; Homero Schiabel; Nelson D A Mascarenhas; Lucas R Borges
Journal:  J Digit Imaging       Date:  2013-04       Impact factor: 4.056

7.  Computer-aided detection of breast masses depicted on full-field digital mammograms: a performance assessment.

Authors:  B Zheng; J H Sumkin; M L Zuley; D Lederman; X Wang; D Gur
Journal:  Br J Radiol       Date:  2011-02-22       Impact factor: 3.039

8.  Effect of breast density on computer aided detection.

Authors:  Ansgar Malich; Dorothee R Fischer; Mirjam Facius; Alexander Petrovitch; Joachim Boettcher; Christiane Marx; Andreas Hansch; Werner A Kaiser
Journal:  J Digit Imaging       Date:  2005-09       Impact factor: 4.056

9.  "Hippocrates-mst": a prototype for computer-aided microcalcification analysis and risk assessment for breast cancer.

Authors:  George Spyrou; Smaragda Kapsimalakou; Antonis Frigas; Konstantinos Koufopoulos; Stamatios Vassilaros; Panos Ligomenides
Journal:  Med Biol Eng Comput       Date:  2006-10-27       Impact factor: 2.602

10.  Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts.

Authors:  Kevin M Kelly; Judy Dean; W Scott Comulada; Sung-Jae Lee
Journal:  Eur Radiol       Date:  2009-09-02       Impact factor: 5.315

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