Literature DB >> 30713851

Impact of prevalence and case distribution in lab-based diagnostic imaging studies.

Brandon D Gallas1, Weijie Chen1, Elodia Cole2, Robert Ochs3, Nicholas Petrick1, Etta D Pisano2, Berkman Sahiner1, Frank W Samuelson1, Kyle J Myers1.   

Abstract

We investigated effects of prevalence and case distribution on radiologist diagnostic performance as measured by area under the receiver operating characteristic curve (AUC) and sensitivity-specificity in lab-based reader studies evaluating imaging devices. Our retrospective reader studies compared full-field digital mammography (FFDM) to screen-film mammography (SFM) for women with dense breasts. Mammograms were acquired from the prospective Digital Mammographic Imaging Screening Trial. We performed five reader studies that differed in terms of cancer prevalence and the distribution of noncancers. Twenty radiologists participated in each reader study. Using split-plot study designs, we collected recall decisions and multilevel scores from the radiologists for calculating sensitivity, specificity, and AUC. Differences in reader-averaged AUCs slightly favored SFM over FFDM (biggest AUC difference: 0.047, SE = 0.023 , p = 0.047 ), where standard error accounts for reader and case variability. The differences were not significant at a level of 0.01 (0.05/5 reader studies). The differences in sensitivities and specificities were also indeterminate. Prevalence had little effect on AUC (largest difference: 0.02), whereas sensitivity increased and specificity decreased as prevalence increased. We found that AUC is robust to changes in prevalence, while radiologists were more aggressive with recall decisions as prevalence increased.

Entities:  

Keywords:  area under the receiver operating characteristic curve; image evaluation; multiple-reader, multiple-case analysis; sensitivity; specificity; study design

Year:  2019        PMID: 30713851      PMCID: PMC6340399          DOI: 10.1117/1.JMI.6.1.015501

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  24 in total

1.  From the laboratory to the clinic: the "prevalence effect".

Authors:  David Gur; Howard E Rockette; Thomas Warfel; Joan M Lacomis; Carl R Fuhrman
Journal:  Acad Radiol       Date:  2003-11       Impact factor: 3.173

2.  Prevalence effect in a laboratory environment.

Authors:  David Gur; Howard E Rockette; Derek R Armfield; Arye Blachar; Jennifer K Bogan; Giuseppe Brancatelli; Cynthia A Britton; Manuel L Brown; Peter L Davis; James V Ferris; Carl R Fuhrman; Sara K Golla; Sanj Katyal; Joan M Lacomis; Barry M McCook; F Leland Thaete; Thomas E Warfel
Journal:  Radiology       Date:  2003-07       Impact factor: 11.105

3.  Cognitive psychology: rare items often missed in visual searches.

Authors:  Jeremy M Wolfe; Todd S Horowitz; Naomi M Kenner
Journal:  Nature       Date:  2005-05-26       Impact factor: 49.962

4.  The prevalence effect in a laboratory environment: Changing the confidence ratings.

Authors:  David Gur; Andriy I Bandos; Carl R Fuhrman; Amy H Klym; Jill L King; Howard E Rockette
Journal:  Acad Radiol       Date:  2007-01       Impact factor: 3.173

5.  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

6.  Multireader multicase variance analysis for binary data.

Authors:  Brandon D Gallas; Gene A Pennello; Kyle J Myers
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  2007-12       Impact factor: 2.129

7.  Reader studies for validation of CAD systems.

Authors:  Brandon D Gallas; David G Brown
Journal:  Neural Netw       Date:  2007-12-23

8.  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

9.  Full-field digital versus screen-film mammography: comparative accuracy in concurrent screening cohorts.

Authors:  Marco Rosselli Del Turco; Paola Mantellini; Stefano Ciatto; Rita Bonardi; Francesca Martinelli; Barbara Lazzari; Nehmat Houssami
Journal:  AJR Am J Roentgenol       Date:  2007-10       Impact factor: 3.959

10.  ACRIN--lessons learned in conducting multi-center trials of imaging and cancer.

Authors:  Bruce J Hillman
Journal:  Cancer Imaging       Date:  2005-11-23       Impact factor: 3.909

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  4 in total

1.  Advancing Research on Medical Image Perception by Strengthening Multidisciplinary Collaboration.

Authors:  Melissa Treviño; George Birdsong; Ann Carrigan; Peter Choyke; Trafton Drew; Miguel Eckstein; Anna Fernandez; Brandon D Gallas; Maryellen Giger; Stephen M Hewitt; Todd S Horowitz; Yuhong V Jiang; Bonnie Kudrick; Susana Martinez-Conde; Stephen Mitroff; Linda Nebeling; Joseph Saltz; Frank Samuelson; Steven E Seltzer; Behrouz Shabestari; Lalitha Shankar; Eliot Siegel; Mike Tilkin; Jennifer S Trueblood; Alison L Van Dyke; Aradhana M Venkatesan; David Whitney; Jeremy M Wolfe
Journal:  JNCI Cancer Spectr       Date:  2022-01-05

2.  Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach.

Authors:  Abdul Rahman Diab; Bryan Haslam; Jiye G Kim; William Lotter; Giorgia Grisot; Eric Wu; Kevin Wu; Jorge Onieva Onieva; Yun Boyer; Jerrold L Boxerman; Meiyun Wang; Mack Bandler; Gopal R Vijayaraghavan; A Gregory Sorensen
Journal:  Nat Med       Date:  2021-01-11       Impact factor: 87.241

Review 3.  A review of explainable and interpretable AI with applications in COVID-19 imaging.

Authors:  Jordan D Fuhrman; Naveena Gorre; Qiyuan Hu; Hui Li; Issam El Naqa; Maryellen L Giger
Journal:  Med Phys       Date:  2021-12-07       Impact factor: 4.506

4.  A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study.

Authors:  Sarah N Dudgeon; Si Wen; Matthew G Hanna; Rajarsi Gupta; Mohamed Amgad; Manasi Sheth; Hetal Marble; Richard Huang; Markus D Herrmann; Clifford H Szu; Darick Tong; Bruce Werness; Evan Szu; Denis Larsimont; Anant Madabhushi; Evangelos Hytopoulos; Weijie Chen; Rajendra Singh; Steven N Hart; Ashish Sharma; Joel Saltz; Roberto Salgado; Brandon D Gallas
Journal:  J Pathol Inform       Date:  2021-11-15
  4 in total

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