Literature DB >> 26158048

Generalized Roe and Metz receiver operating characteristic model: analytic link between simulated decision scores and empirical AUC variances and covariances.

Brandon D Gallas1, Stephen L Hillis2.   

Abstract

Modeling and simulation are often used to understand and investigate random quantities and estimators. In 1997, Roe and Metz introduced a simulation model to validate analysis methods for the popular endpoint in reader studies to evaluate medical imaging devices, the reader-averaged area under the receiver operating characteristic (ROC) curve. Here, we generalize the notation of the model to allow more flexibility in recognition that variances of ROC ratings depend on modality and truth state. We also derive and validate equations for computing population variances and covariances for reader-averaged empirical AUC estimates under the generalized model. The equations are one-dimensional integrals that can be calculated using standard numerical integration techniques. This work provides the theoretical foundation and validation for a Java application called iRoeMetz that can simulate multireader multicase ROC studies and numerically calculate the corresponding variances and covariances of the empirical AUC. The iRoeMetz application and source code can be found at the "iMRMC" project on the google code project hosting site. These results and the application can be used by investigators to investigate ROC endpoints, validate analysis methods, and plan future studies.

Keywords:  medical imaging; multireader multicase; radiology; reader studies; receiver operating characteristic curves; simulations

Year:  2014        PMID: 26158048      PMCID: PMC4478859          DOI: 10.1117/1.JMI.1.3.031006

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


  28 in total

1.  Continuous versus categorical data for ROC analysis: some quantitative considerations.

Authors:  R F Wagner; S V Beiden; C E Metz
Journal:  Acad Radiol       Date:  2001-04       Impact factor: 3.173

2.  Components-of-variance models and multiple-bootstrap experiments: an alternative method for random-effects, receiver operating characteristic analysis.

Authors:  S V Beiden; R F Wagner; G Campbell
Journal:  Acad Radiol       Date:  2000-05       Impact factor: 3.173

3.  A contaminated binormal model for ROC data: Part II. A formal model.

Authors:  D D Dorfman; K S Berbaum
Journal:  Acad Radiol       Date:  2000-06       Impact factor: 3.173

4.  Components-of-variance models for random-effects ROC analysis: the case of unequal variance structures across modalities.

Authors:  S V Beiden; R F Wagner; G Campbell; C E Metz; Y Jiang
Journal:  Acad Radiol       Date:  2001-07       Impact factor: 3.173

5.  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

6.  A comparison of denominator degrees of freedom methods for multiple observer ROC analysis.

Authors:  Stephen L Hillis
Journal:  Stat Med       Date:  2007-02-10       Impact factor: 2.373

7.  Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis.

Authors:  Stephen L Hillis; Kevin S Berbaum; Charles E Metz
Journal:  Acad Radiol       Date:  2008-05       Impact factor: 3.173

8.  Dorfman-Berbaum-Metz method for statistical analysis of multireader, multimodality receiver operating characteristic data: validation with computer simulation.

Authors:  C A Roe; C E Metz
Journal:  Acad Radiol       Date:  1997-04       Impact factor: 3.173

9.  Statistical power considerations for a utility endpoint in observer performance studies.

Authors:  Craig K Abbey; Frank W Samuelson; Brandon D Gallas
Journal:  Acad Radiol       Date:  2013-04-20       Impact factor: 3.173

10.  Reliable and computationally efficient maximum-likelihood estimation of "proper" binormal ROC curves.

Authors:  Lorenzo L Pesce; Charles E Metz
Journal:  Acad Radiol       Date:  2007-07       Impact factor: 3.173

View more
  10 in total

1.  Multireader multicase reader studies with binary agreement data: simulation, analysis, validation, and sizing.

Authors:  Weijie Chen; Adam Wunderlich; Nicholas Petrick; Brandon D Gallas
Journal:  J Med Imaging (Bellingham)       Date:  2014-12-04

2.  Paired split-plot designs of multireader multicase studies.

Authors:  Weijie Chen; Qi Gong; Brandon D Gallas
Journal:  J Med Imaging (Bellingham)       Date:  2018-05-17

3.  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

4.  Determining Roe and Metz model parameters for simulating multireader multicase confidence-of-disease rating data based on real-data or conjectured Obuchowski-Rockette parameter estimates.

Authors:  Stephen L Hillis; Brian J Smith; Weijie Chen
Journal:  J Med Imaging (Bellingham)       Date:  2022-07-08

5.  Identical-test Roe and Metz simulation model for validating multi-reader methods of analysis for comparing different radiologic imaging modalities.

Authors:  Stephen L Hillis
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2022-04-04

6.  Relationship between Roe and Metz simulation model for multireader diagnostic data and Obuchowski-Rockette model parameters.

Authors:  Stephen L Hillis
Journal:  Stat Med       Date:  2018-04-02       Impact factor: 2.373

7.  Differentiation of ductal carcinoma in-situ from benign micro-calcifications by dedicated breast computed tomography.

Authors:  Shadi Aminololama-Shakeri; Craig K Abbey; Peymon Gazi; Nicolas D Prionas; Anita Nosratieh; Chin-Shang Li; John M Boone; Karen K Lindfors
Journal:  Eur J Radiol       Date:  2015-10-01       Impact factor: 3.528

8.  Improving the Performance of Radiologists Using Artificial Intelligence-Based Detection Support Software for Mammography: A Multi-Reader Study.

Authors:  Jeong Hoon Lee; Ki Hwan Kim; Eun Hye Lee; Jong Seok Ahn; Jung Kyu Ryu; Young Mi Park; Gi Won Shin; Young Joong Kim; Hye Young Choi
Journal:  Korean J Radiol       Date:  2022-04-04       Impact factor: 7.109

9.  Development and Validation of a Convolutional Neural Network for Automated Detection of Scaphoid Fractures on Conventional Radiographs.

Authors:  Nils Hendrix; Ernst Scholten; Bastiaan Vernhout; Stefan Bruijnen; Bas Maresch; Mathijn de Jong; Suzanne Diepstraten; Stijn Bollen; Steven Schalekamp; Maarten de Rooij; Alexander Scholtens; Ward Hendrix; Tijs Samson; Lee-Ling Sharon Ong; Eric Postma; Bram van Ginneken; Matthieu Rutten
Journal:  Radiol Artif Intell       Date:  2021-04-28

10.  Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories.

Authors:  Tina M Morrison; Pras Pathmanathan; Mariam Adwan; Edward Margerrison
Journal:  Front Med (Lausanne)       Date:  2018-09-25
  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.