Literature DB >> 19727750

Computer-aided detection (CAD) for breast MRI: evaluation of efficacy at 3.0 T.

Carla Meeuwis1, Stephanie M van de Ven, Gerard Stapper, Arancha M Fernandez Gallardo, Maurice A A J van den Bosch, Willem P Th M Mali, Wouter B Veldhuis.   

Abstract

OBJECTIVE: The purpose of the study was to evaluate the accuracy of 3.0-T breast MRI interpretation using manual and fully automated kinetic analyses.
MATERIAL AND METHODS: Manual MRI interpretation was done on an Advantage Workstation. Retrospectively, all examinations were processed with a computer-aided detection (CAD) system. CAD data sets were interpreted by two experienced breast radiologists and two residents. For each lesion automated analysis of enhancement kinetics was evaluated at 50% and 100% thresholds. Forty-nine malignant and 22 benign lesions were evaluated.
RESULTS: Using threshold enhancement alone, the sensitivity and specificity of CAD were 97.9% and 86.4%, respectively, for the 50% threshold, and 97.9% and 90%, respectively, for the 100% threshold. Manual interpretation by two breast radiologists showed a sensitivity of 84.6% and a specificity of 68.8%. For the same two radiologists the mean sensitivity and specificity for CAD-based interpretation was 90.4% (not significant) and 81.3% (significant at p < 0.05), respectively. With one-way ANOVA no significant differences were found between the two breast radiologists and the two residents together, or between any two readers separately.
CONCLUSION: CAD-based analysis improved the specificity compared with manual analysis of enhancement. Automated analysis at 50% and 100% thresholds showed a high sensitivity and specificity for readers with varying levels of experience.

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Mesh:

Year:  2009        PMID: 19727750      PMCID: PMC2822230          DOI: 10.1007/s00330-009-1573-5

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  11 in total

1.  Development, standardization, and testing of a lexicon for reporting contrast-enhanced breast magnetic resonance imaging studies.

Authors:  D M Ikeda; N M Hylton; K Kinkel; M G Hochman; C K Kuhl; W A Kaiser; J C Weinreb; S F Smazal; H Degani; P Viehweg; J Barclay; M D Schnall
Journal:  J Magn Reson Imaging       Date:  2001-06       Impact factor: 4.813

2.  Additional breast lesions in patients eligible for breast-conserving therapy by MRI: impact on preoperative management and potential benefit of computerised analysis.

Authors:  Eline E Deurloo; Johannes L Peterse; Emiel J Th Rutgers; Albert P E Besnard; Sara H Muller; Kenneth G A Gilhuijs
Journal:  Eur J Cancer       Date:  2005-07       Impact factor: 9.162

3.  Breast MRI lesion classification: improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system.

Authors:  Lina Arbash Meinel; Alan H Stolpen; Kevin S Berbaum; Laurie L Fajardo; Joseph M Reinhardt
Journal:  J Magn Reson Imaging       Date:  2007-01       Impact factor: 4.813

4.  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

5.  Meta-analysis of MR imaging in the diagnosis of breast lesions.

Authors:  Nicky H G M Peters; Inne H M Borel Rinkes; Nicolaas P A Zuithoff; Willem P T M Mali; Karel G M Moons; Petra H M Peeters
Journal:  Radiology       Date:  2007-11-16       Impact factor: 11.105

6.  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

7.  Assessment of suspected breast cancer by MRI: a prospective clinical trial using a combined kinetic and morphologic analysis.

Authors:  Jonathan I Wiener; Kathy J Schilling; Carol Adami; Nancy A Obuchowski
Journal:  AJR Am J Roentgenol       Date:  2005-03       Impact factor: 3.959

8.  Clinically and mammographically occult breast lesions on MR images: potential effect of computerized assessment on clinical reading.

Authors:  Eline E Deurloo; Sara H Muller; Johannes L Peterse; Albert P E Besnard; Kenneth G A Gilhuijs
Journal:  Radiology       Date:  2005-01-13       Impact factor: 11.105

9.  Characterization of breast lesion morphology with delayed 3DSSMT: an adjunct to dynamic breast MRI.

Authors:  C S Leong; B L Daniel; R J Herfkens; R L Birdwell; S S Jeffrey; D M Ikeda; A M Sawyer-Glover; G H Glover
Journal:  J Magn Reson Imaging       Date:  2000-02       Impact factor: 4.813

10.  Assessment of three different software systems in the evaluation of dynamic MRI of the breast.

Authors:  K D Kurz; D Steinhaus; V Klar; M Cohnen; H J Wittsack; A Saleh; U Mödder; D Blondin
Journal:  Eur J Radiol       Date:  2007-12-03       Impact factor: 3.528

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  18 in total

1.  Radio-guided occult lesion localisation for breast lesions under computer-aided MRI guidance: the first experience and initial results.

Authors:  M H Yilmaz; F Kilic; G E Icten; F Aydogan; V Ozben; M Halac; D C Olgun; E Gazioglu; V Celik; C Uras; Z A Altug
Journal:  Br J Radiol       Date:  2011-10-18       Impact factor: 3.039

2.  Current Status and New Developments in Breast MRI.

Authors:  Katja C Siegmann; Bernhard Krämer; Claus Claussen
Journal:  Breast Care (Basel)       Date:  2011-04-29       Impact factor: 2.860

3.  Size assessment of breast lesions by means of a computer-aided detection (CAD) system for magnetic resonance mammography.

Authors:  G Levrini; R Sghedoni; C Mori; A Botti; R Vacondio; A Nitrosi; M Iori; F Nicoli
Journal:  Radiol Med       Date:  2011-03-19       Impact factor: 3.469

Review 4.  Digital Analysis in Breast Imaging.

Authors:  Giovanna Negrão de Figueiredo; Michael Ingrisch; Eva Maria Fallenberg
Journal:  Breast Care (Basel)       Date:  2019-06-04       Impact factor: 2.860

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

Authors:  Joann Pan; Basak E Dogan; Selin Carkaci; Lumarie Santiago; Elsa Arribas; Scott B Cantor; Wei Wei; R Jason Stafford; Gary J Whitman
Journal:  J Digit Imaging       Date:  2013-10       Impact factor: 4.056

6.  Microcirculatory fraction (MCF(I)) as a potential imaging marker for tumor heterogeneity in breast cancer.

Authors:  Xiangyu Yang; Ewa Mrozek; Maryam Lustberg; Guang Jia; Steffen Sammet; Christina Sammet; Charles Shapiro; Michael V Knopp
Journal:  Magn Reson Imaging       Date:  2012-08-11       Impact factor: 2.546

7.  Accuracy and interpretation time of computer-aided detection among novice and experienced breast MRI readers.

Authors:  Constance D Lehman; Jeffrey D Blume; Wendy B DeMartini; Nola M Hylton; Benjamin Herman; Mitchell D Schnall
Journal:  AJR Am J Roentgenol       Date:  2013-06       Impact factor: 3.959

8.  Prediction of pathological complete response of breast cancer patients undergoing neoadjuvant chemotherapy: usefulness of breast MRI computer-aided detection.

Authors:  H Kim; H H Kim; J S Park; H J Shin; J H Cha; E Y Chae; W J Choi
Journal:  Br J Radiol       Date:  2014-08-27       Impact factor: 3.039

9.  Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study.

Authors:  Shannon C Agner; Mark A Rosen; Sarah Englander; John E Tomaszewski; Michael D Feldman; Paul Zhang; Carolyn Mies; Mitchell D Schnall; Anant Madabhushi
Journal:  Radiology       Date:  2014-03-10       Impact factor: 11.105

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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