Literature DB >> 15649087

Computer Aided Detection (CAD) for breast MRI.

Chris Wood1.   

Abstract

Since 1999, there has been a 40 percent increase per year in the number of breast MR studies performed in the United States. In addition, over 1200 sites in the United States have purchased surface coils for use in breast MR. This number is expected to grow to over 2,000 coils by the end of 2007. It is well accepted that MR sensitivity for invasive breast cancers is near 100%, but as the use of breast MRI increases, radiologists interpreting breast MR are challenged to achieve high specificity while retaining high sensitivity. Reading the large number of acquired MR images in a reasonable amount of time also becomes more important as the number of studies increases. Breast MR acquisition and image interpretation techniques have been refined through clinical optimization. The number of images to interpret, however, has increased to several hundred per case. Computer Aided Detection (CAD) algorithms have allowed radiologists to regain efficiency while maintaining optimized acquisition techniques. The first CAD system for breast MR (CADstream by Confirma, Inc.) was launched in January 2003. The CAD installed base has since grown to over 150 systems in the US. The primary reason for this quick adoption of CAD for breast MR is that the CAD software enables readers to increase their efficiency while potentially improving their overall accuracy. The full benefits CAD for Breast MR are realized when the interpreting radiologist has a thorough understanding of the algorithms used, and the limitations of CAD.

Entities:  

Mesh:

Year:  2005        PMID: 15649087     DOI: 10.1177/153303460500400107

Source DB:  PubMed          Journal:  Technol Cancer Res Treat        ISSN: 1533-0338


  9 in total

Review 1.  [Quantitative parametric analysis of contrast-enhanced lesions in dynamic MR mammography].

Authors:  E A M Hauth; H Jaeger; S Maderwald; A Mühler; R Kimmig; M Forsting
Journal:  Radiologe       Date:  2008-06       Impact factor: 0.635

2.  Computer-aided detection of metastatic brain tumors using automated three-dimensional template matching.

Authors:  Robert D Ambrosini; Peng Wang; Walter G O'Dell
Journal:  J Magn Reson Imaging       Date:  2010-01       Impact factor: 4.813

Review 3.  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

Review 4.  BI-RADS 3 Assessment on MRI: A Lesion-Based Review for Breast Radiologists.

Authors:  Derek L Nguyen; Kelly S Myers; Eniola Oluyemi; Lisa A Mullen; Babita Panigrahi; Joanna Rossi; Emily B Ambinder
Journal:  J Breast Imaging       Date:  2022-06-28

5.  Morphologic blooming in breast MRI as a characterization of margin for discriminating benign from malignant lesions.

Authors:  Alan Penn; Scott Thompson; Rachel Brem; Constance Lehman; Paul Weatherall; Mitchell Schnall; Gillian Newstead; Emily Conant; Susan Ascher; Elizabeth Morris; Etta Pisano
Journal:  Acad Radiol       Date:  2006-11       Impact factor: 3.173

6.  Kinetic volume analysis on dynamic contrast-enhanced MRI of triple-negative breast cancer: associations with survival outcomes.

Authors:  Yoko Hayashi; Hiroko Satake; Satoko Ishigaki; Rintaro Ito; Mariko Kawamura; Hisashi Kawai; Shingo Iwano; Shinji Naganawa
Journal:  Br J Radiol       Date:  2019-12-16       Impact factor: 3.039

Review 7.  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

8.  Kinetic Features of Invasive Breast Cancers on Computer-Aided Diagnosis Using 3T MRI Data: Correlation with Clinical and Pathologic Prognostic Factors.

Authors:  Sung Eun Song; Kyu Ran Cho; Bo Kyoung Seo; Ok Hee Woo; Seung Pil Jung; Deuk Jae Sung
Journal:  Korean J Radiol       Date:  2019-03       Impact factor: 3.500

9.  Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR: a machine learning approach.

Authors:  Hongmin Cai; Yanxia Peng; Caiwen Ou; Minsheng Chen; Li Li
Journal:  PLoS One       Date:  2014-01-31       Impact factor: 3.240

  9 in total

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