Literature DB >> 23589186

Comparing performance of the CADstream and the DynaCAD breast MRI CAD systems : CADstream vs. DynaCAD in breast MRI.

Joann Pan1, Basak E Dogan, Selin Carkaci, Lumarie Santiago, Elsa Arribas, Scott B Cantor, Wei Wei, R Jason Stafford, Gary J Whitman.   

Abstract

Computer-aided diagnosis (CAD) systems are software programs that use algorithms to find patterns associated with breast cancer on breast magnetic resonance imaging (MRI). The most commonly used CAD systems in the USA are CADstream (CS) (Merge Healthcare Inc., Chicago, IL) and DynaCAD for Breast (DC) (Invivo, Gainesville, FL). Our primary objective in this study was to compare the CS and DC breast MRI CAD systems for diagnostic accuracy and postprocessed image quality. Our secondary objective was to compare the evaluation times of radiologists using each system. Three radiologists evaluated 30 biopsy-proven malignant lesions and 29 benign lesions on CS and DC and rated the lesions' malignancy status using the Breast Imaging Reporting and Data System. Image quality was ranked on a 0-5 scale, and mean reading times were also recorded. CS detected 70 % of the malignant and 32 % of the benign lesions while DC detected 81 % of the malignant lesions and 34 % of the benign lesions. Analysis of the area under the receiver operating characteristic curve revealed that the difference in diagnostic performance was not statistically significant. On image quality scores, CS had significantly higher volume rendering (VR) (p < 0.0001) and motion correction (MC) scores (p < 0.0001). There were no statistically significant differences in the remaining image quality scores. Differences in evaluation times between DC and CS were also not statistically significant. We conclude that both CS and DC perform similarly in aiding detection of breast cancer on MRI. MRI CAD selection will likely be based on other factors, such as user interface and image quality preferences, including MC and VR.

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Year:  2013        PMID: 23589186      PMCID: PMC3782607          DOI: 10.1007/s10278-013-9602-y

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


  17 in total

1.  Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions?

Authors:  C K Kuhl; P Mielcareck; S Klaschik; C Leutner; E Wardelmann; J Gieseke; H H Schild
Journal:  Radiology       Date:  1999-04       Impact factor: 11.105

2.  Sensitivity of MRI versus mammography for detecting foci of multifocal, multicentric breast cancer in Fatty and dense breasts using the whole-breast pathologic examination as a gold standard.

Authors:  Francesco Sardanelli; Gian M Giuseppetti; Pietro Panizza; Massimo Bazzocchi; Alfonso Fausto; Giovanni Simonetti; Vincenzo Lattanzio; Alessandro Del Maschio
Journal:  AJR Am J Roentgenol       Date:  2004-10       Impact factor: 3.959

3.  A new automated software system to evaluate breast MR examinations: improved specificity without decreased sensitivity.

Authors:  Constance D Lehman; Sue Peacock; Wendy B DeMartini; Xiaoming Chen
Journal:  AJR Am J Roentgenol       Date:  2006-07       Impact factor: 3.959

4.  Breast MR imaging: computer-aided evaluation program for discriminating benign from malignant lesions.

Authors:  Teresa C Williams; Wendy B DeMartini; Savannah C Partridge; Sue Peacock; Constance D Lehman
Journal:  Radiology       Date:  2007-05-16       Impact factor: 11.105

Review 5.  Computer-aided diagnosis in medical imaging: historical review, current status and future potential.

Authors:  Kunio Doi
Journal:  Comput Med Imaging Graph       Date:  2007-03-08       Impact factor: 4.790

6.  MRI for diagnosis of pure ductal carcinoma in situ: a prospective observational study.

Authors:  Christiane K Kuhl; Simone Schrading; Heribert B Bieling; Eva Wardelmann; Claudia C Leutner; Roy Koenig; Walther Kuhn; Hans H Schild
Journal:  Lancet       Date:  2007-08-11       Impact factor: 79.321

7.  False-negative MR imaging of malignant breast tumors.

Authors:  C Boetes; S P Strijk; R Holland; J O Barentsz; R F Van Der Sluis; J H Ruijs
Journal:  Eur Radiol       Date:  1997       Impact factor: 5.315

8.  Breast MR imaging screening in 192 women proved or suspected to be carriers of a breast cancer susceptibility gene: preliminary results.

Authors:  C K Kuhl; R K Schmutzler; C C Leutner; A Kempe; E Wardelmann; A Hocke; M Maringa; U Pfeifer; D Krebs; H H Schild
Journal:  Radiology       Date:  2000-04       Impact factor: 11.105

9.  Computer-aided detection applied to breast MRI: assessment of CAD-generated enhancement and tumor sizes in breast cancers before and after neoadjuvant chemotherapy.

Authors:  Wendy B Demartini; Constance D Lehman; Sue Peacock; Mai T Russell
Journal:  Acad Radiol       Date:  2005-07       Impact factor: 3.173

Review 10.  Computer-aided detection in breast MRI: a systematic review and meta-analysis.

Authors:  Monique D Dorrius; Marijke C Jansen-van der Weide; Peter M A van Ooijen; Ruud M Pijnappel; Matthijs Oudkerk
Journal:  Eur Radiol       Date:  2011-03-15       Impact factor: 5.315

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  2 in total

1.  Evaluation of Kinetic Entropy of Breast Masses Initially Found on MRI using Whole-lesion Curve Distribution Data: Comparison with the Standard Kinetic Analysis.

Authors:  Akiko Shimauchi; Hiroyuki Abe; David V Schacht; Jian Yulei; Federico D Pineda; Sanaz A Jansen; Rajiv Ganesh; Gillian M Newstead
Journal:  Eur Radiol       Date:  2015-02-20       Impact factor: 5.315

2.  Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs.

Authors:  X-X Yin; S Hadjiloucas; J-H Chen; Y Zhang; J-L Wu; M-Y Su
Journal:  PLoS One       Date:  2017-03-10       Impact factor: 3.240

  2 in total

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