Literature DB >> 22939365

Computed-aided diagnosis (CAD) in the detection of breast cancer.

C Dromain1, B Boyer, R Ferré, S Canale, S Delaloge, C Balleyguier.   

Abstract

Computer-aided detection (CAD) systems have been developed for interpretation to improve mammographic detection of breast cancer at screening by reducing the number of false-negative interpretation that can be caused by subtle findings, radiologist distraction and complex architecture. They use a digitized mammographic image that can be obtained from both screen-film mammography and full field digital mammography. Its performance in breast cancer detection is dependent on the performance of the CAD itself, the population to which it is applied and the radiologists who use it. There is a clear benefit to the use of CAD in less experienced radiologist and in detecting breast carcinomas presenting as microcalcifications. This review gives a detailed description CAD systems used in mammography and their performance in assistance of reading in screening mammography and as an alternative to double reading. Other CAD systems developed for MRI and ultrasound are also presented and discussed.
Copyright © 2012. Published by Elsevier Ireland Ltd.

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Mesh:

Year:  2012        PMID: 22939365     DOI: 10.1016/j.ejrad.2012.03.005

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  36 in total

1.  MicroRNA-155 complementation on a chemically functionalized dual electrode surface for determining breast cancer.

Authors:  Subash C B Gopinath; Veeradasan Perumal; Shijin Xuan
Journal:  3 Biotech       Date:  2020-05-28       Impact factor: 2.406

Review 2.  Imaging informatics: essential tools for the delivery of imaging services.

Authors:  David S Mendelson; Daniel L Rubin
Journal:  Acad Radiol       Date:  2013-10       Impact factor: 3.173

3.  A Standard Mammography Unit - Standard 3D Ultrasound Probe Fusion Prototype: First Results.

Authors:  Rüdiger Schulz-Wendtland; Sebastian M Jud; Peter A Fasching; Arndt Hartmann; Marcus Radicke; Claudia Rauh; Michael Uder; Marius Wunderle; Paul Gass; Hanna Langemann; Matthias W Beckmann; Julius Emons
Journal:  Geburtshilfe Frauenheilkd       Date:  2017-04-27       Impact factor: 2.915

Review 4.  Current Applications and Future Impact of Machine Learning in Radiology.

Authors:  Garry Choy; Omid Khalilzadeh; Mark Michalski; Synho Do; Anthony E Samir; Oleg S Pianykh; J Raymond Geis; Pari V Pandharipande; James A Brink; Keith J Dreyer
Journal:  Radiology       Date:  2018-06-26       Impact factor: 11.105

5.  Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?

Authors:  Bibo Shi; Lars J Grimm; Maciej A Mazurowski; Jay A Baker; Jeffrey R Marks; Lorraine M King; Carlo C Maley; E Shelley Hwang; Joseph Y Lo
Journal:  Acad Radiol       Date:  2017-05-11       Impact factor: 3.173

6.  Breast cancer molecular subtype classifier that incorporates MRI features.

Authors:  Elizabeth J Sutton; Brittany Z Dashevsky; Jung Hun Oh; Harini Veeraraghavan; Aditya P Apte; Sunitha B Thakur; Elizabeth A Morris; Joseph O Deasy
Journal:  J Magn Reson Imaging       Date:  2016-01-12       Impact factor: 4.813

7.  A new and fast image feature selection method for developing an optimal mammographic mass detection scheme.

Authors:  Maxine Tan; Jiantao Pu; Bin Zheng
Journal:  Med Phys       Date:  2014-08       Impact factor: 4.071

8.  [Future of mammography-based imaging].

Authors:  R Schulz-Wendtland; T Wittenberg; T Michel; A Hartmann; M W Beckmann; C Rauh; S M Jud; B Brehm; M Meier-Meitinger; G Anton; M Uder; P A Fasching
Journal:  Radiologe       Date:  2014-03       Impact factor: 0.635

9.  Identification and segmentation of obscure pectoral muscle in mediolateral oblique mammograms.

Authors:  Chia-Hung Wei; Chih-Ying Gwo; Pai Jung Huang
Journal:  Br J Radiol       Date:  2016-04-04       Impact factor: 3.039

10.  Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

Authors:  Bibo Shi; Lars J Grimm; Maciej A Mazurowski; Jay A Baker; Jeffrey R Marks; Lorraine M King; Carlo C Maley; E Shelley Hwang; Joseph Y Lo
Journal:  J Am Coll Radiol       Date:  2018-02-02       Impact factor: 5.532

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