Literature DB >> 19864510

Experimental design and data analysis in receiver operating characteristic studies: lessons learned from reports in radiology from 1997 to 2006.

Junji Shiraishi1, Lorenzo L Pesce, Charles E Metz, Kunio Doi.   

Abstract

PURPOSE: To provide a broad perspective concerning the recent use of receiver operating characteristic (ROC) analysis in medical imaging by reviewing ROC studies published in Radiology between 1997 and 2006 for experimental design, imaging modality, medical condition, and ROC paradigm.
MATERIALS AND METHODS: Two hundred ninety-five studies were obtained by conducting a literature search with PubMed with two criteria: publication in Radiology between 1997 and 2006 and occurrence of the phrase "receiver operating characteristic." Studies returned by the query that were not diagnostic imaging procedure performance evaluations were excluded. Characteristics of the remaining studies were tabulated.
RESULTS: Two hundred thirty-three (79.0%) of the 295 studies reported findings based on observers' diagnostic judgments or objective measurements. Forty-three (14.6%) did not include human observers, with most of these reporting an evaluation of a computer-aided diagnosis system or functional data obtained with computed tomography (CT) or magnetic resonance (MR) imaging. The remaining 19 (6.4%) studies were classified as reviews or meta-analyses and were excluded from our subsequent analysis. Among the various imaging modalities, MR imaging (46.0%) and CT (25.7%) were investigated most frequently. Approximately 60% (144 of 233) of ROC studies with human observers published in Radiology included three or fewer observers.
CONCLUSION: ROC analysis is widely used in radiologic research, confirming its fundamental role in assessing diagnostic performance. However, the ROC studies reported in Radiology were not always adequate to support clear and clinically relevant conclusions.

Entities:  

Mesh:

Year:  2009        PMID: 19864510      PMCID: PMC2786192          DOI: 10.1148/radiol.2533081632

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


  23 in total

1.  A contaminated binormal model for ROC data: Part I. Some interesting examples of binormal degeneracy.

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

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

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

Review 3.  Assessment of medical imaging and computer-assist systems: lessons from recent experience.

Authors:  Robert F Wagner; Sergey V Beiden; Gregory Campbell; Charles E Metz; William M Sacks
Journal:  Acad Radiol       Date:  2002-11       Impact factor: 3.173

4.  Sample size estimation: a glimpse beyond simple formulas.

Authors:  John Eng
Journal:  Radiology       Date:  2004-03       Impact factor: 11.105

Review 5.  Assessment of medical imaging systems and computer aids: a tutorial review.

Authors:  Robert F Wagner; Charles E Metz; Gregory Campbell
Journal:  Acad Radiol       Date:  2007-06       Impact factor: 3.173

6.  Assessment of radiologic tests: control of bias and other design considerations.

Authors:  C B Begg; B J McNeil
Journal:  Radiology       Date:  1988-05       Impact factor: 11.105

7.  Signal detectability and medical decision-making.

Authors:  L B Lusted
Journal:  Science       Date:  1971-03-26       Impact factor: 47.728

Review 8.  Measuring the accuracy of diagnostic systems.

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

9.  Multireader receiver operating characteristic studies: a comparison of study designs.

Authors:  N A Obuchowski
Journal:  Acad Radiol       Date:  1995-08       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

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  19 in total

1.  A nonparametric procedure for comparing the areas under correlated LROC curves.

Authors:  Adam Wunderlich; Frédéric Noo
Journal:  IEEE Trans Med Imaging       Date:  2012-06-18       Impact factor: 10.048

2.  Improved detection of focal pneumonia by chest radiography with bone suppression imaging.

Authors:  Feng Li; Roger Engelmann; Lorenzo Pesce; Samuel G Armato; Heber Macmahon
Journal:  Eur Radiol       Date:  2012-07-05       Impact factor: 5.315

3.  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

4.  Enhancement of breast CADx with unlabeled data.

Authors:  Andrew R Jamieson; Maryellen L Giger; Karen Drukker; Lorenzo L Pesce
Journal:  Med Phys       Date:  2010-08       Impact factor: 4.071

5.  Noise injection for training artificial neural networks: a comparison with weight decay and early stopping.

Authors:  Richard M Zur; Yulei Jiang; Lorenzo L Pesce; Karen Drukker
Journal:  Med Phys       Date:  2009-10       Impact factor: 4.071

Review 6.  Clinical research and diagnostic efficacy studies in the oral and maxillofacial radiology literature: 1996-2005.

Authors:  I H Kim; M J Patel; S L Hirt; M L Kantor
Journal:  Dentomaxillofac Radiol       Date:  2011-07       Impact factor: 2.419

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

Authors:  Junji Shiraishi; Daisuke Fukuoka; Reimi Iha; Haruka Inada; Rie Tanaka; Takeshi Hara
Journal:  Radiol Phys Technol       Date:  2018-09-22

8.  Is liver perfusion CT reproducible? A study on intra- and interobserver agreement of normal hepatic haemodynamic parameters obtained with two different software packages.

Authors:  Elisa Almeida Sathler Bretas; Ulysses S Torres; Lucas Rios Torres; Daniel Bekhor; Celso Fernando Saito Filho; Douglas Jorge Racy; Lorenzo Faggioni; Giuseppe D'Ippolito
Journal:  Br J Radiol       Date:  2017-08-22       Impact factor: 3.039

9.  Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI.

Authors:  Andreas M Rauschecker; Jeffrey D Rudie; Long Xie; Jiancong Wang; Michael Tran Duong; Emmanuel J Botzolakis; Asha M Kovalovich; John Egan; Tessa C Cook; R Nick Bryan; Ilya M Nasrallah; Suyash Mohan; James C Gee
Journal:  Radiology       Date:  2020-04-07       Impact factor: 11.105

Review 10.  Radiological images and machine learning: Trends, perspectives, and prospects.

Authors:  Zhenwei Zhang; Ervin Sejdić
Journal:  Comput Biol Med       Date:  2019-02-27       Impact factor: 4.589

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