Literature DB >> 11222075

Radiologist detection of microcalcifications with and without computer-aided detection: a comparative study.

R F Brem1, J M Schoonjans.   

Abstract

AIM: To compare the sensitivity and specificity of microcalcification detection by radiologists alone and assisted by a computer-aided detection (CAD) system.
MATERIALS AND METHODS: Films of 106 patients were masked, randomized, digitized and analysed by the CAD-system. Five readers interpreted the original mammograms and were blinded to demographics, medical history and earlier films. Forty-two mammograms with malignant microcalcifications, 40 with benign microcalcifications and 24 normal mammograms were included. Results were recorded on a standardized image interpretation form. The mammograms with suspicious areas flagged by the CAD-system were displayed on mini-monitors and immediately re-reviewed. The interpretation was again recorded on a new copy of the standard form and classified according to six groups.
RESULTS: Forty-one out of 42 (98%) malignant microcalcifications and 32 of 40 (80%) benign microcalcifications were flagged by the CAD-system. There was an average of 1.2 markers per image. The sensitivity for malignant microcalcifications detection by mammographers without and with the CAD-system ranged from 81% to 98% and from 88% to 98%, respectively. The mean difference without and with CAD-system was 2.2% (range 0-7%).
CONCLUSION: No statistically significant changes in sensitivity were found when experienced mammographers were assisted by the CAD-system, with no significant compromise in specificity. Copyright 2001 The Royal College of Radiologists.

Entities:  

Mesh:

Year:  2001        PMID: 11222075     DOI: 10.1053/crad.2000.0592

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  5 in total

Review 1.  CAD for mammography: the technique, results, current role and further developments.

Authors:  Ansgar Malich; Dorothee R Fischer; Joachim Böttcher
Journal:  Eur Radiol       Date:  2006-01-17       Impact factor: 5.315

2.  Analog Computer-Aided Detection (CAD) information can be more effective than binary marks.

Authors:  Corbin A Cunningham; Trafton Drew; Jeremy M Wolfe
Journal:  Atten Percept Psychophys       Date:  2017-02       Impact factor: 2.199

3.  Influence of computer-aided detection on performance of screening mammography.

Authors:  Joshua J Fenton; Stephen H Taplin; Patricia A Carney; Linn Abraham; Edward A Sickles; Carl D'Orsi; Eric A Berns; Gary Cutter; R Edward Hendrick; William E Barlow; Joann G Elmore
Journal:  N Engl J Med       Date:  2007-04-05       Impact factor: 91.245

4.  Breast imaging: how we manage diagnostic technology at a multidisciplinary breast center.

Authors:  Alejandro Tejerina Bernal; Antonio Tejerina Bernal; Francisco Rabadán Doreste; Ana De Lara González; Juan Antonio Roselló Llerena; Armando Tejerina Gómez
Journal:  J Oncol       Date:  2012-07-05       Impact factor: 4.375

5.  Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis.

Authors:  Aryan Mobiny; Aditi Singh; Hien Van Nguyen
Journal:  J Clin Med       Date:  2019-08-17       Impact factor: 4.241

  5 in total

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