Literature DB >> 23091171

Computer-aided detection of masses at mammography: interactive decision support versus prompts.

Rianne Hupse1, Maurice Samulski, Marc B Lobbes, Ritse M Mann, Roel Mus, Gerard J den Heeten, David Beijerinck, Ruud M Pijnappel, Carla Boetes, Nico Karssemeijer.   

Abstract

PURPOSE: To compare effectiveness of an interactive computer-aided detection (CAD) system, in which CAD marks and their associated suspiciousness scores remain hidden unless their location is queried by the reader, with the effect of traditional CAD prompts used in current clinical practice for the detection of malignant masses on full-field digital mammograms.
MATERIALS AND METHODS: The requirement for institutional review board approval was waived for this retrospective observer study. Nine certified screening radiologists and three residents who were trained in breast imaging read 200 studies (63 studies containing at least one screen-detected mass, 17 false-negative studies, 20 false-positive studies, and 100 normal studies) twice, once with CAD prompts and once with interactive CAD. Localized findings were reported and scored by the readers. In the prompted mode, findings were recorded before and after activation of CAD. The partial area under the location receiver operating characteristic (ROC) curve for an interval of low false-positive fractions typical for screening, from 0 to 0.2, was computed for each reader and each mode. Differences in reader performance were analyzed by using software.
RESULTS: The average partial area under the location ROC curve with unaided reading was 0.57, and it increased to 0.62 with interactive CAD, while it remained unaffected by prompts. The difference in reader performance for unaided reading versus interactive CAD was statistically significant (P = .009).
CONCLUSION: When used as decision support, interactive use of CAD for malignant masses on mammograms may be more effective than the current use of CAD, which is aimed at the prevention of perceptual oversights. RSNA, 2012

Mesh:

Year:  2012        PMID: 23091171     DOI: 10.1148/radiol.12120218

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


  14 in total

1.  Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.

Authors:  Alejandro Rodriguez-Ruiz; Kristina Lång; Albert Gubern-Merida; Mireille Broeders; Gisella Gennaro; Paola Clauser; Thomas H Helbich; Margarita Chevalier; Tao Tan; Thomas Mertelmeier; Matthew G Wallis; Ingvar Andersson; Sophia Zackrisson; Ritse M Mann; Ioannis Sechopoulos
Journal:  J Natl Cancer Inst       Date:  2019-09-01       Impact factor: 13.506

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.  New methods for using computer-aided detection information for the detection of lung nodules on chest radiographs.

Authors:  S Schalekamp; B van Ginneken; Bgf Heggelman; M Imhof-Tas; I Somers; M Brink; M Spee; Cm Schaefer-Prokop; N Karssemeijer
Journal:  Br J Radiol       Date:  2014-02-17       Impact factor: 3.039

4.  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

5.  A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

Authors:  Yuchen Qiu; Shiju Yan; Rohith Reddy Gundreddy; Yunzhi Wang; Samuel Cheng; Hong Liu; Bin Zheng
Journal:  J Xray Sci Technol       Date:  2017       Impact factor: 1.535

Review 6.  Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.

Authors:  Krzysztof J Geras; Ritse M Mann; Linda Moy
Journal:  Radiology       Date:  2019-09-24       Impact factor: 11.105

Review 7.  Is the false-positive rate in mammography in North America too high?

Authors:  Michelle T Le; Carmel E Mothersill; Colin B Seymour; Fiona E McNeill
Journal:  Br J Radiol       Date:  2016-06-08       Impact factor: 3.039

8.  Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool.

Authors:  Serena Pacilè; January Lopez; Pauline Chone; Thomas Bertinotti; Jean Marie Grouin; Pierre Fillard
Journal:  Radiol Artif Intell       Date:  2020-11-04

Review 9.  Multi-reader multi-case studies using the area under the receiver operator characteristic curve as a measure of diagnostic accuracy: systematic review with a focus on quality of data reporting.

Authors:  Thaworn Dendumrongsup; Andrew A Plumb; Steve Halligan; Thomas R Fanshawe; Douglas G Altman; Susan Mallett
Journal:  PLoS One       Date:  2014-12-26       Impact factor: 3.240

10.  Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study.

Authors:  Alejandro Rodriguez-Ruiz; Kristina Lång; Albert Gubern-Merida; Jonas Teuwen; Mireille Broeders; Gisella Gennaro; Paola Clauser; Thomas H Helbich; Margarita Chevalier; Thomas Mertelmeier; Matthew G Wallis; Ingvar Andersson; Sophia Zackrisson; Ioannis Sechopoulos; Ritse M Mann
Journal:  Eur Radiol       Date:  2019-04-16       Impact factor: 5.315

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