Literature DB >> 11719657

Soft-copy mammographic readings with different computer-assisted detection cuing environments: preliminary findings.

B Zheng1, M A Ganott, C A Britton, C M Hakim, L A Hardesty, T S Chang, H E Rockette, D Gur.   

Abstract

PURPOSE: To assess the performance of radiologists in the detection of masses and microcalcification clusters on digitized mammograms by using different computer-assisted detection (CAD) cuing environments.
MATERIALS AND METHODS: Two hundred nine digitized mammograms depicting 57 verified masses and 38 microcalcification clusters in 85 positive and 35 negative cases were interpreted independently by seven radiologists using five display modes. Except for the first mode, for which no CAD results were provided, suspicious regions identified with a CAD scheme were cued in all the other modes by using a combination of two cuing sensitivities (90% and 50%) and two false-positive rates (0.5 and 2.0 per image). A receiver operating characteristic study was performed by using soft-copy images.
RESULTS: CAD cuing at 90% sensitivity and a rate of 0.5 false-positive region per image improved observer performance levels significantly (P < .01). As accuracy of CAD cuing decreased so did observer performances (P < .01). Cuing specificity affected mass detection more significantly, while cuing sensitivity affected detection of microcalcification clusters more significantly (P < .01). Reduction of cuing sensitivity and specificity significantly increased false-negative rates in noncued areas (P < .05). Trends were consistent for all observers.
CONCLUSION: CAD systems have the potential to significantly improve diagnostic performance in mammography. However, poorly performing schemes could adversely affect observer performance in both cued and noncued areas.

Mesh:

Year:  2001        PMID: 11719657     DOI: 10.1148/radiol.2213010308

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  13 in total

1.  Soft copy versus hard copy reading in digital mammography.

Authors:  Silvia Obenauer; Klaus-Peter Hermann; Katharina Marten; Susanne Luftner-Nagel; Dorit von Heyden; Per Skaane; Eckhardt Grabbe
Journal:  J Digit Imaging       Date:  2004-01-30       Impact factor: 4.056

2.  Evaluation of computer-aided detection and diagnosis systems.

Authors:  Nicholas Petrick; Berkman Sahiner; Samuel G Armato; Alberto Bert; Loredana Correale; Silvia Delsanto; Matthew T Freedman; David Fryd; David Gur; Lubomir Hadjiiski; Zhimin Huo; Yulei Jiang; Lia Morra; Sophie Paquerault; Vikas Raykar; Frank Samuelson; Ronald M Summers; Georgia Tourassi; Hiroyuki Yoshida; Bin Zheng; Chuan Zhou; Heang-Ping Chan
Journal:  Med Phys       Date:  2013-08       Impact factor: 4.071

3.  False positive marks on unsuspicious screening mammography with computer-aided detection.

Authors:  Mary C Mahoney; Karthikeyan Meganathan
Journal:  J Digit Imaging       Date:  2011-10       Impact factor: 4.056

Review 4.  Automation bias: a systematic review of frequency, effect mediators, and mitigators.

Authors:  Kate Goddard; Abdul Roudsari; Jeremy C Wyatt
Journal:  J Am Med Inform Assoc       Date:  2011-06-16       Impact factor: 4.497

5.  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

6.  Improving the performance of computer-aided detection of subtle breast masses using an adaptive cueing method.

Authors:  Xingwei Wang; Lihua Li; Weidong Xu; Wei Liu; Dror Lederman; Bin Zheng
Journal:  Phys Med Biol       Date:  2012-01-21       Impact factor: 3.609

7.  Computer-aided detection of breast masses depicted on full-field digital mammograms: a performance assessment.

Authors:  B Zheng; J H Sumkin; M L Zuley; D Lederman; X Wang; D Gur
Journal:  Br J Radiol       Date:  2011-02-22       Impact factor: 3.039

8.  Importance of Better Human-Computer Interaction in the Era of Deep Learning: Mammography Computer-Aided Diagnosis as a Use Case.

Authors:  Robert M Nishikawa; Kyongtae T Bae
Journal:  J Am Coll Radiol       Date:  2017-10-31       Impact factor: 5.532

9.  A new approach to develop computer-aided detection schemes of digital mammograms.

Authors:  Maxine Tan; Wei Qian; Jiantao Pu; Hong Liu; Bin Zheng
Journal:  Phys Med Biol       Date:  2015-05-18       Impact factor: 3.609

10.  Short-term outcomes of screening mammography using computer-aided detection: a population-based study of medicare enrollees.

Authors:  Joshua J Fenton; Guibo Xing; Joann G Elmore; Heejung Bang; Steven L Chen; Karen K Lindfors; Laura-Mae Baldwin
Journal:  Ann Intern Med       Date:  2013-04-16       Impact factor: 25.391

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