Literature DB >> 20429765

Comparison of two software versions of a commercially available computer-aided detection (CAD) system for detecting breast cancer.

Seung Ja Kim1, Woo Kyung Moon, Soo-Yeon Kim, Jung Min Chang, Sun Mi Kim, Nariya Cho.   

Abstract

BACKGROUND: The performance of the computer-aided detection (CAD) system can be determined by the sensitivity and false-positive marks rate, therefore these factors should be improved by upgrading the software version of the CAD system.
PURPOSE: To compare retrospectively the performances of two software versions of a commercially available CAD system when applied to full-field digital mammograms for the detection of breast cancers in a screening group.
MATERIAL AND METHODS: Versions 3.1 and 8.3 of a CAD software system (ImageChecker, R2 Technology) were applied to the full-field digital mammograms of 130 women (age range 36-80, mean age 53 years) with 130 breast cancers detected by screening.
RESULTS: The overall sensitivities of the version 3.1 and 8.3 CAD systems were 92.3% (120 of 130) and 96.2% (125 of 130) (P=0.025), respectively, and sensitivities for masses were 78.3% (36 of 46) and 89.1% (41 of 46) (P=0.024) and for microcalcifications 100% (84 of 84) and 100% (84 of 84), respectively. Version 8.3 correctly marked five lesions of invasive ductal carcinoma that were missed by version 3.1. Average numbers of false-positive marks per image were 0.38 (0.15 for calcifications, 0.23 for masses) for version 3.1 and 0.46 (0.13 for calcifications, 0.33 for masses) for version 8.3 (P=0.1420).
CONCLUSION: The newer version 8.3 of the CAD system showed better overall sensitivity for the detection of breast cancer than version 3.1 due to its improved sensitivity for masses when applied to full-field digital mammograms.

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Year:  2010        PMID: 20429765     DOI: 10.3109/02841851003709490

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  4 in total

1.  [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

2.  Effectiveness of computer-aided detection in community mammography practice.

Authors:  Joshua J Fenton; Linn Abraham; Stephen H Taplin; Berta M Geller; Patricia A Carney; Carl D'Orsi; Joann G Elmore; William E Barlow
Journal:  J Natl Cancer Inst       Date:  2011-07-27       Impact factor: 13.506

3.  Role of computer-aided detection in very small screening detected invasive breast cancers.

Authors:  Xavier Bargalló; Martín Velasco; Gorane Santamaría; Montse Del Amo; Pedro Arguis; Sonia Sánchez Gómez
Journal:  J Digit Imaging       Date:  2013-06       Impact factor: 4.056

4.  Unintended consequences of machine learning in medicine?

Authors:  Laura McDonald; Sreeram V Ramagopalan; Andrew P Cox; Mustafa Oguz
Journal:  F1000Res       Date:  2017-09-19
  4 in total

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