Literature DB >> 15827823

Effect of breast density on computer aided detection.

Ansgar Malich1, Dorothee R Fischer, Mirjam Facius, Alexander Petrovitch, Joachim Boettcher, Christiane Marx, Andreas Hansch, Werner A Kaiser.   

Abstract

PURPOSE: This study was conducted to assess the clinical impact of breast density and density of the lesion's background on the performance of a computer-aided detection (CAD) system in the detection of breast masses (MA) and microcalcifications (MC).
MATERIALS AND METHODS: A total of 200 screening mammograms interpreted as BI-RADS 1 and suspicious mammograms of 150 patients having a histologically verified malignancy from 1992 to 2000 were selected by using a sampler of tumor cases. Excluding those cases having more than one lesion or a contralateral malignancy attributable to statistical reasons, 127 cases with 127 malignant findings were analyzed with a CAD system (Second Look 5.0, CADx Systems, Inc., Beavercreek, OH). Of the 127 malignant lesions, 56 presented as MC and 101 presented as MA, including 30 cases with both malignant signs. Overall breast density of the mammogram and density of the lesion's background were determined by two observers in congruence (density a: entirely fatty, density b: scattered fibroglandular tissue, density c: heterogeneously dense, density d: extremely dense).
RESULTS: Within the unsuspicious group, 100/200 cases did not have any CAD MA marks and were therefore truly negative (specificity 50%), and 151/200 cases did not have any CAD MC marks (specificity 75.5%). For these 200 cases, the numbers of marks per image were 0.41 and 0.37 (density a), 0.38 and 0.97 (density b), 0.44 and 0.91 (density c), and 0.58 and 0.68 (density d) for MC and MA marks, respectively (Fisher's t-test: n.s. for MC, p < 0.05 for MA). Malignant lesions were correctly detected in at least one view by the CAD system for 52/56 (92.8%) MC and 91/101 (90.1%) MA. Detection rate versus breast density was: 4/6 (66.7%) and 18/19 (94.7%) (density a), 32/33 (97.0%) and 49/51 (96.1%) (density b), 14/15 (93.3%) and 23/28 (82.1%) (density c), and 2/2 (100%) and 1/3 (33.3%) (density d) for MC and MA, respectively. Detection rate versus the lesion's background was: 19/21 (90.5%) and 36/38 (94.7%) (density a), 34/36 (94.4%) and 59/62 (95.2%) (density b), 8/9 (88.9%) and 20/24 (83.3%) (density c), and 9/10 (90%) and 4/8 (50%) (density d) for groups 2 and 3, respectively. Detection rates differed significantly for masses in heterogeneously dense and extremely dense tissue (overall or lesion's background) versus all other densities (Fisher's t-test: p < 0.05). A significantly lowered FP rate for masses was found on mammograms of entirely fatty tissue.
CONCLUSION: Overall breast density and density at a lesion's background do not appear to have a significant effect on CAD sensitivity or specificity for MC. CAD sensitivity for MA may be lowered in cases with heterogeneously and extremely dense breasts, and CAD specificity for MA is highest in cases with extremely fatty breasts. The effects of overall breast density and density of a lesion's background appear to be similar.

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Year:  2005        PMID: 15827823      PMCID: PMC3046715          DOI: 10.1007/s10278-004-1047-x

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


  22 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.  Are unnecessary follow-up procedures induced by computer-aided diagnosis (CAD) in mammography? Comparison of mammographic diagnosis with and without use of CAD.

Authors:  Christiane Marx; Ansgar Malich; Mirjam Facius; Uta Grebenstein; Dieter Sauner; Stefan O R Pfleiderer; Werner A Kaiser
Journal:  Eur J Radiol       Date:  2004-07       Impact factor: 3.528

3.  Professional quality assurance for mammography screening programs.

Authors:  R E Bird
Journal:  Radiology       Date:  1990-11       Impact factor: 11.105

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

Authors:  H P Chan; K Doi; C J Vyborny; R A Schmidt; C E Metz; K L Lam; T Ogura; Y Z Wu; H MacMahon
Journal:  Invest Radiol       Date:  1990-10       Impact factor: 6.016

Review 5.  Computer-assisted reading of mammograms.

Authors:  N Karssemeijer; J H Hendriks
Journal:  Eur Radiol       Date:  1997       Impact factor: 5.315

6.  Computer-aided detection of clustered microcalcifications on digitized mammograms: a robustness experiment.

Authors:  Y H Chang; B Zheng; D Gur
Journal:  Acad Radiol       Date:  1997-06       Impact factor: 3.173

7.  Effect of case selection on the performance of computer-aided detection schemes.

Authors:  R M Nishikawa; M L Giger; K Doi; C E Metz; F F Yin; C J Vyborny; R A Schmidt
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8.  Influence of breast lesion size and histologic findings on tumor detection rate of a computer-aided detection system.

Authors:  Ansgar Malich; Dieter Sauner; Christiane Marx; Mirjam Facius; Thomas Boehm; Stefan O Pfleiderer; Marlies Fleck; Werner A Kaiser
Journal:  Radiology       Date:  2003-07-17       Impact factor: 11.105

9.  Benefit of independent double reading in a population-based mammography screening program.

Authors:  E L Thurfjell; K A Lernevall; A A Taube
Journal:  Radiology       Date:  1994-04       Impact factor: 11.105

10.  Comparison of standard reading and computer aided diagnosis (CAD) on a proficiency test of screening mammography.

Authors:  Stefano Ciatto; Beniamino Brancato; Marco Rosselli Del Turco; Gabriella Risso; Sandra Catarzi; Daniela Morrone; Daniela Bricolo; Marco Zappa
Journal:  Radiol Med       Date:  2003 Jul-Aug       Impact factor: 3.469

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  6 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

2.  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

3.  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

4.  Library based x-ray scatter correction for dedicated cone beam breast CT.

Authors:  Linxi Shi; Srinivasan Vedantham; Andrew Karellas; Lei Zhu
Journal:  Med Phys       Date:  2016-08       Impact factor: 4.071

5.  Current and Future Methods for Measuring Breast Density: A Brief Comparative Review.

Authors:  Mark A Sak; Peter J Littrup; Neb Duric; Maeve Mullooly; Mark E Sherman; Gretchen L Gierach
Journal:  Breast Cancer Manag       Date:  2015-08-28

6.  Variable size computer-aided detection prompts and mammography film reader decisions.

Authors:  Fiona J Gilbert; Susan M Astley; Caroline Rm Boggis; Magnus A McGee; Pamela M Griffiths; Stephen W Duffy; Olorunsola F Agbaje; Maureen Gc Gillan; Mary Wilson; Anil K Jain; Nicola Barr; Ursula M Beetles; Miriam A Griffiths; Jill Johnson; Rita M Roberts; Heather E Deans; Karen A Duncan; Geeta Iyengar
Journal:  Breast Cancer Res       Date:  2008-08-25       Impact factor: 6.466

  6 in total

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