Literature DB >> 25247960

Impact of computer-aided detection systems on radiologist accuracy with digital mammography.

Elodia B Cole1, Zheng Zhang, Helga S Marques, R Edward Hendrick, Martin J Yaffe, Etta D Pisano.   

Abstract

OBJECTIVE: The purpose of this study was to assess the impact of computer-aided detection (CAD) systems on the performance of radiologists with digital mammograms acquired during the Digital Mammographic Imaging Screening Trial (DMIST).
MATERIALS AND METHODS: Only those DMIST cases with proven cancer status by biopsy or 1-year follow-up that had available digital images were included in this multireader, multicase ROC study. Two commercially available CAD systems for digital mammography were used: iCAD SecondLook, version 1.4; and R2 ImageChecker Cenova, version 1.0. Fourteen radiologists interpreted, without and with CAD, a set of 300 cases (150 cancer, 150 benign or normal) on the iCAD SecondLook system, and 15 radiologists interpreted a different set of 300 cases (150 cancer, 150 benign or normal) on the R2 ImageChecker Cenova system.
RESULTS: The average AUC was 0.71 (95% CI, 0.66-0.76) without and 0.72 (95% CI, 0.67-0.77) with the iCAD system (p = 0.07). Similarly, the average AUC was 0.71 (95% CI, 0.66-0.76) without and 0.72 (95% CI 0.67-0.77) with the R2 system (p = 0.08). Sensitivity and specificity differences without and with CAD for both systems also were not significant.
CONCLUSION: Radiologists in our studies rarely changed their diagnostic decisions after the addition of CAD. The application of CAD had no statistically significant effect on radiologist AUC, sensitivity, or specificity performance with digital mammograms from DMIST.

Entities:  

Keywords:  AUC; computer-aided detection; mammography; sensitivity; specificity

Mesh:

Year:  2014        PMID: 25247960      PMCID: PMC4286296          DOI: 10.2214/AJR.12.10187

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  22 in total

1.  Variability in mammogram interpretation.

Authors:  C Beam; D Sullivan
Journal:  Adm Radiol J       Date:  1996-09

2.  CAD-aided mammogram training.

Authors:  Ping Luo; Wei Qian; Pat Romilly
Journal:  Acad Radiol       Date:  2005-08       Impact factor: 3.173

3.  American College of Radiology Imaging Network digital mammographic imaging screening trial: objectives and methodology.

Authors:  Etta D Pisano; Constantine A Gatsonis; Martin J Yaffe; R Edward Hendrick; Anna N A Tosteson; Dennis G Fryback; Lawrence W Bassett; Janet K Baum; Emily F Conant; Roberta A Jong; Murray Rebner; Carl J D'Orsi
Journal:  Radiology       Date:  2005-06-16       Impact factor: 11.105

4.  Comparison of soft-copy and hard-copy reading for full-field digital mammography.

Authors:  Robert M Nishikawa; Suddhasatta Acharyya; Constantine Gatsonis; Etta D Pisano; Elodia B Cole; Helga S Marques; Carl J D'Orsi; Dione M Farria; Kalpana M Kanal; Mary C Mahoney; Murray Rebner; Melinda J Staiger
Journal:  Radiology       Date:  2009-04       Impact factor: 11.105

5.  Accuracy of soft-copy digital mammography versus that of screen-film mammography according to digital manufacturer: ACRIN DMIST retrospective multireader study.

Authors:  R Edward Hendrick; Elodia B Cole; Etta D Pisano; Suddhasatta Acharyya; Helga Marques; Michael A Cohen; Roberta A Jong; Gordon E Mawdsley; Kalpana M Kanal; Carl J D'Orsi; Murray Rebner; Constantine Gatsonis
Journal:  Radiology       Date:  2008-04       Impact factor: 11.105

6.  Computer-aided detection (CAD) in mammography: does it help the junior or the senior radiologist?

Authors:  Corinne Balleyguier; Karen Kinkel; Jacques Fermanian; Sebastien Malan; Germaine Djen; Patrice Taourel; Olivier Helenon
Journal:  Eur J Radiol       Date:  2005-04       Impact factor: 3.528

7.  Single reading with computer-aided detection and double reading of screening mammograms in the United Kingdom National Breast Screening Program.

Authors:  Fiona J Gilbert; Susan M Astley; Magnus A McGee; Maureen G C Gillan; Caroline R M Boggis; Pamela M Griffiths; Stephen W Duffy
Journal:  Radiology       Date:  2006-10       Impact factor: 11.105

8.  Does computer-aided detection assist in the early detection of breast cancer?

Authors:  K Hukkinen; M Pamilo
Journal:  Acta Radiol       Date:  2005-04       Impact factor: 1.990

9.  Diagnostic performance of digital versus film mammography for breast-cancer screening.

Authors:  Etta D Pisano; Constantine Gatsonis; Edward Hendrick; Martin Yaffe; Janet K Baum; Suddhasatta Acharyya; Emily F Conant; Laurie L Fajardo; Lawrence Bassett; Carl D'Orsi; Roberta Jong; Murray Rebner
Journal:  N Engl J Med       Date:  2005-09-16       Impact factor: 91.245

10.  Assessing the stand-alone sensitivity of computer-aided detection with cancer cases from the Digital Mammographic Imaging Screening Trial.

Authors:  Elodia B Cole; Zheng Zhang; Helga S Marques; Robert M Nishikawa; R Edward Hendrick; Martin J Yaffe; Wittaya Padungchaichote; Cherie Kuzmiak; Jatuporn Chayakulkheeree; Emily F Conant; Laurie L Fajardo; Janet Baum; Constantine Gatsonis; Etta Pisano
Journal:  AJR Am J Roentgenol       Date:  2012-09       Impact factor: 3.959

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  14 in total

1.  Utilization and Cost of Mammography Screening Among Commercially Insured Women 50 to 64 Years of Age in the United States, 2012-2016.

Authors:  Jaya S Khushalani; Donatus U Ekwueme; Thomas B Richards; Susan A Sabatino; Gery P Guy; Yuanhui Zhang; Florence Tangka
Journal:  J Womens Health (Larchmt)       Date:  2019-10-15       Impact factor: 2.681

Review 2.  CAD and AI for breast cancer-recent development and challenges.

Authors:  Heang-Ping Chan; Ravi K Samala; Lubomir M Hadjiiski
Journal:  Br J Radiol       Date:  2019-12-16       Impact factor: 3.039

3.  A half-second glimpse often lets radiologists identify breast cancer cases even when viewing the mammogram of the opposite breast.

Authors:  Karla K Evans; Tamara Miner Haygood; Julie Cooper; Anne-Marie Culpan; Jeremy M Wolfe
Journal:  Proc Natl Acad Sci U S A       Date:  2016-08-29       Impact factor: 11.205

4.  Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection.

Authors:  Constance D Lehman; Robert D Wellman; Diana S M Buist; Karla Kerlikowske; Anna N A Tosteson; Diana L Miglioretti
Journal:  JAMA Intern Med       Date:  2015-11       Impact factor: 21.873

Review 5.  Artificial intelligence in radiology.

Authors:  Ahmed Hosny; Chintan Parmar; John Quackenbush; Lawrence H Schwartz; Hugo J W L Aerts
Journal:  Nat Rev Cancer       Date:  2018-08       Impact factor: 60.716

Review 6.  Artificial Intelligence: A Primer for Breast Imaging Radiologists.

Authors:  Manisha Bahl
Journal:  J Breast Imaging       Date:  2020-06-19

Review 7.  Deep Learning in Medical Image Analysis.

Authors:  Heang-Ping Chan; Ravi K Samala; Lubomir M Hadjiiski; Chuan Zhou
Journal:  Adv Exp Med Biol       Date:  2020       Impact factor: 2.622

8.  Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study.

Authors:  Eun-Kyung Kim; Hyo-Eun Kim; Kyunghwa Han; Bong Joo Kang; Yu-Mee Sohn; Ok Hee Woo; Chan Wha Lee
Journal:  Sci Rep       Date:  2018-02-09       Impact factor: 4.379

9.  Improved Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography.

Authors:  Alyssa T Watanabe; Vivian Lim; Hoanh X Vu; Richard Chim; Eric Weise; Jenna Liu; William G Bradley; Christopher E Comstock
Journal:  J Digit Imaging       Date:  2019-08       Impact factor: 4.056

10.  Simple eye-movement feedback during visual search is not helpful.

Authors:  Trafton Drew; Lauren H Williams
Journal:  Cogn Res Princ Implic       Date:  2017-11-22
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