Literature DB >> 30244314

Verification of modified receiver-operating characteristic software using simulated rating data.

Junji Shiraishi1, Daisuke Fukuoka2, Reimi Iha3, Haruka Inada3, Rie Tanaka4, Takeshi Hara5.   

Abstract

ROCKIT, which is a receiver-operating characteristic (ROC) curve-fitting software package, was developed by Metz et al. In the early 1990s, it is a very frequently used ROC software throughout the world. In addition to ROCKIT, DBM-MRMC software was developed for multi-reader multi-case analysis of the difference in average area under ROC curves (AUCs). Because this old software cannot run on a PC with Windows 7 or a more recent operating system, we developed new software that employs the same basic algorithms with minor modifications. In this study, we verified our modified software and tested the differences between the index of diagnostic accuracies using simulated rating data. In our simulation model, all data were generated using target AUCs and a binormal parameter b. In ROC curve fitting with simulated rating data, we varied four factors: the total number of case samples, the ratio of positive-to-negative cases, a binormal parameter b, and the preset AUC. To investigate the differences between the statistical test results obtained from our software and the existing software, we generated simulated rating data sets with three levels of case difficulty and three degrees of difference in AUCs obtained from two modalities. As a result of the simulation, the AUCs estimated by the new and existing software were highly correlated (R > 0.98), and there were high agreements (85% or more) in the statistical test results. In conclusion, we believe that our modified software is as capable as the existing software.

Keywords:  Binormal distribution; Computer software; Multi-reader multi-case; Observer study; Receiver-operating characteristic analysis (ROC); Simulation data

Mesh:

Year:  2018        PMID: 30244314     DOI: 10.1007/s12194-018-0479-9

Source DB:  PubMed          Journal:  Radiol Phys Technol        ISSN: 1865-0333


  23 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.  Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules.

Authors:  J Shiraishi; S Katsuragawa; J Ikezoe; T Matsumoto; T Kobayashi; K Komatsu; M Matsui; H Fujita; Y Kodera; K Doi
Journal:  AJR Am J Roentgenol       Date:  2000-01       Impact factor: 3.959

3.  Receiver operating characteristic curves and their use in radiology.

Authors:  Nancy A Obuchowski
Journal:  Radiology       Date:  2003-10       Impact factor: 11.105

4.  Basic concepts and development of an all-purpose computer interface for ROC/FROC observer study.

Authors:  Junji Shiraishi; Daisuke Fukuoka; Takeshi Hara; Hiroyuki Abe
Journal:  Radiol Phys Technol       Date:  2012-07-05

Review 5.  ROC analysis in medical imaging: a tutorial review of the literature.

Authors:  Charles E Metz
Journal:  Radiol Phys Technol       Date:  2007-10-27

Review 6.  Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems.

Authors:  Charles E Metz
Journal:  J Am Coll Radiol       Date:  2006-06       Impact factor: 5.532

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.  Semiparametric estimation of the relationship between ROC operating points and the test-result scale: application to the proper binormal model.

Authors:  Lorenzo L Pesce; Karla Horsch; Karen Drukker; Charles E Metz
Journal:  Acad Radiol       Date:  2011-12       Impact factor: 3.173

9.  Radiographic applications of receiver operating characteristic (ROC) curves.

Authors:  D J Goodenough; K Rossmann; L B Lusted
Journal:  Radiology       Date:  1974-01       Impact factor: 11.105

Review 10.  Measuring the accuracy of diagnostic systems.

Authors:  J A Swets
Journal:  Science       Date:  1988-06-03       Impact factor: 47.728

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  1 in total

1.  Deep learning versus the human visual system for detecting motion blur in radiography.

Authors:  Rie Tanaka; Shiho Nozaki; Futa Goshima; Junji Shiraishi
Journal:  J Med Imaging (Bellingham)       Date:  2022-01-18
  1 in total

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