Literature DB >> 20033580

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

Stamatia Destounis1, Sarah Hanson, Renee Morgan, Philip Murphy, Patricia Somerville, Posy Seifert, Valerie Andolina, Andrea Arieno, Melissa Skolny, Wende Logan-Young.   

Abstract

PURPOSE: A retrospective evaluation of the ability of computer-aided detection (CAD) ability to identify breast carcinoma in standard mammographic projections.
MATERIALS AND METHODS: Forty-five biopsy proven lesions in 44 patients imaged digitally with CAD applied at examination were reviewed. Forty-four screening BIRADS category 1 digital mammography examinations were randomly identified to serve as a comparative normal/control population. Data included patient age; BIRADS breast density; lesion type, size, and visibility; number, type, and location of CAD marks per image; CAD ability to mark lesions; needle core and surgical pathologic correlation.
RESULTS: The CAD lesion/case sensitivity of 87% (n = 39), image sensitivity of 69% (n = 31) for mediolateral oblique view and 78% (n = 35) for the craniocaudal view was found. The average false positive rate in 44 normal screening cases was 2.0 (range 1-8). The 2.0 figure is based on 88 reported false positive CAD marks in 44 normal screening exams: 98% (n = 44) lesions proceeded to excision; initial pathology upgraded at surgical excision from in situ to invasive disease in 24% (n = 9) lesions.
CONCLUSION: CAD demonstrated potential to detect mammographically visible cancers in standard projections for all lesion types.

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Year:  2009        PMID: 20033580     DOI: 10.1007/s11548-009-0300-7

Source DB:  PubMed          Journal:  Int J Comput Assist Radiol Surg        ISSN: 1861-6410            Impact factor:   2.924


  14 in total

1.  Potential contribution of computer-aided detection to the sensitivity of screening mammography.

Authors:  L J Warren Burhenne; S A Wood; C J D'Orsi; S A Feig; D B Kopans; K F O'Shaughnessy; E A Sickles; L Tabar; C J Vyborny; R A Castellino
Journal:  Radiology       Date:  2000-05       Impact factor: 11.105

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

Authors:  W T Ho; P W T Lam
Journal:  Clin Radiol       Date:  2003-02       Impact factor: 2.350

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.  Detecting breast cancer not visible by mammography.

Authors:  D B Kopans
Journal:  J Natl Cancer Inst       Date:  1992-05-20       Impact factor: 13.506

Review 5.  The manufacturer's perspective.

Authors:  J Roehrig
Journal:  Br J Radiol       Date:  2005       Impact factor: 3.039

6.  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

7.  Impact of computer-aided detection in a regional screening mammography program.

Authors:  Tommy E Cupples; Joan E Cunningham; James C Reynolds
Journal:  AJR Am J Roentgenol       Date:  2005-10       Impact factor: 3.959

8.  Effect of computer-aided detection on independent double reading of paired screen-film and full-field digital screening mammograms.

Authors:  Per Skaane; Ashwini Kshirsagar; Sandra Stapleton; Kari Young; Ronald A Castellino
Journal:  AJR Am J Roentgenol       Date:  2007-02       Impact factor: 3.959

9.  Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center.

Authors:  T W Freer; M J Ulissey
Journal:  Radiology       Date:  2001-09       Impact factor: 11.105

10.  Geographical distribution of breast cancers on the mammogram: an interval cancer database.

Authors:  M Brown; C Eccles; M G Wallis
Journal:  Br J Radiol       Date:  2001-04       Impact factor: 3.039

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  3 in total

1.  Preclinical evaluation of nuclear morphometry and tissue topology for breast carcinoma detection and margin assessment.

Authors:  Ndeke Nyirenda; Daniel L Farkas; V Krishnan Ramanujan
Journal:  Breast Cancer Res Treat       Date:  2010-05-06       Impact factor: 4.872

2.  Image perception and interpretation of abnormalities; can we believe our eyes? Can we do something about it?

Authors:  Durr-E- Sabih; Ayan Sabih; Quratulain Sabih; Ali N Khan
Journal:  Insights Imaging       Date:  2010-10-24

3.  CAD May Not be Necessary for Microcalcifications in the Digital era, CAD May Benefit Radiologists for Masses.

Authors:  Stamatia V Destounis; Andrea L Arieno; Renee C Morgan
Journal:  J Clin Imaging Sci       Date:  2012-07-28
  3 in total

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