Literature DB >> 16741664

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

Silvia Obenauer1, Christian Sohns, Carola Werner, Eckhardt Grabbe.   

Abstract

The goal of this study was to evaluate the performance of a computer-aided detection (CAD) system in full-field digital mammography (Senographe 2000D, General Electric, Buc, France) in finding out carcinomas depending on the parenchymal density. A total of 226 mediolateral oblique (MLO) and 186 craniocaudal (CC) mammographic views of histologically proven cancers were retrospectively evaluated with a digital CAD system (ImageChecker V2.3 R2 Technology, Los Altos, CA, USA). Malignant tumors were detected correctly by CAD in MLO view in 84.85% in breasts with parenchymal tissue density of the American College of Radiology (ACR) type 1, in 70.33% of the ACR type 2, in 68.12% of the ACR type 3, and in 69.70% of the ACR type 4. For the CC view, similar results were found according to the ACR types. Using the chi-square and McNemar tests, there was no statistical significance. However, a trend of better detection could be seen with decreasing ACR type. In conclusion, there seems to be a tendency for breast tissue density to affect the detection rate of breast cancer when using the CAD system.

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Year:  2006        PMID: 16741664      PMCID: PMC3045151          DOI: 10.1007/s10278-006-0592-x

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  22 in total

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3.  Computer-aided detection in direct digital full-field mammography: initial results.

Authors:  F Baum; U Fischer; S Obenauer; E Grabbe
Journal:  Eur Radiol       Date:  2002-06-12       Impact factor: 5.315

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

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Journal:  Radiology       Date:  2001-04       Impact factor: 11.105

5.  Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion.

Authors:  Jay A Baker; Eric L Rosen; Joseph Y Lo; Edgardo I Gimenez; Ruth Walsh; Mary Scott Soo
Journal:  AJR Am J Roentgenol       Date:  2003-10       Impact factor: 3.959

6.  Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis.

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Journal:  Invest Radiol       Date:  1990-10       Impact factor: 6.016

Review 7.  Screening for breast cancer: how effective are our tests? A critical review.

Authors:  M Moskowitz
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8.  Tumour detection rate of a new commercially available computer-aided detection system.

Authors:  A Malich; C Marx; M Facius; T Boehm; M Fleck; W A Kaiser
Journal:  Eur Radiol       Date:  2001-09-05       Impact factor: 5.315

9.  Improvement in sensitivity of screening mammography with computer-aided detection: a multiinstitutional trial.

Authors:  Rachel F Brem; Janet Baum; Mary Lechner; Stuart Kaplan; Stuart Souders; L Gill Naul; Jeff Hoffmeister
Journal:  AJR Am J Roentgenol       Date:  2003-09       Impact factor: 3.959

10.  Screen film vs full-field digital mammography: image quality, detectability and characterization of lesions.

Authors:  S Obenauer; S Luftner-Nagel; D von Heyden; U Munzel; F Baum; E Grabbe
Journal:  Eur Radiol       Date:  2002-03-19       Impact factor: 5.315

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  6 in total

1.  Thick slices from tomosynthesis data sets: phantom study for the evaluation of different algorithms.

Authors:  Felix Diekmann; Henning Meyer; Susanne Diekmann; Sylvie Puong; Serge Muller; Ulrich Bick; Patrik Rogalla
Journal:  J Digit Imaging       Date:  2007-10-23       Impact factor: 4.056

2.  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

3.  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

4.  Computer-aided detection; the effect of training databases on detection of subtle breast masses.

Authors:  Bin Zheng; Xingwei Wang; Dror Lederman; Jun Tan; David Gur
Journal:  Acad Radiol       Date:  2010-07-22       Impact factor: 3.173

5.  Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer.

Authors:  Jeff Wang; Fumi Kato; Hiroko Yamashita; Motoi Baba; Yi Cui; Ruijiang Li; Noriko Oyama-Manabe; Hiroki Shirato
Journal:  J Digit Imaging       Date:  2017-04       Impact factor: 4.056

6.  Computer-aided detection of breast carcinoma in standard mammographic projections with digital mammography.

Authors:  Stamatia Destounis; Sarah Hanson; Renee Morgan; Philip Murphy; Patricia Somerville; Posy Seifert; Valerie Andolina; Andrea Arieno; Melissa Skolny; Wende Logan-Young
Journal:  Int J Comput Assist Radiol Surg       Date:  2009-04-15       Impact factor: 2.924

  6 in total

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