Meta-analysis of the technical performance of an imaging procedure: guidelines and statistical methodology.

Published

Journal Article (Review)

Medical imaging serves many roles in patient care and the drug approval process, including assessing treatment response and guiding treatment decisions. These roles often involve a quantitative imaging biomarker, an objectively measured characteristic of the underlying anatomic structure or biochemical process derived from medical images. Before a quantitative imaging biomarker is accepted for use in such roles, the imaging procedure to acquire it must undergo evaluation of its technical performance, which entails assessment of performance metrics such as repeatability and reproducibility of the quantitative imaging biomarker. Ideally, this evaluation will involve quantitative summaries of results from multiple studies to overcome limitations due to the typically small sample sizes of technical performance studies and/or to include a broader range of clinical settings and patient populations. This paper is a review of meta-analysis procedures for such an evaluation, including identification of suitable studies, statistical methodology to evaluate and summarize the performance metrics, and complete and transparent reporting of the results. This review addresses challenges typical of meta-analyses of technical performance, particularly small study sizes, which often causes violations of assumptions underlying standard meta-analysis techniques. Alternative approaches to address these difficulties are also presented; simulation studies indicate that they outperform standard techniques when some studies are small. The meta-analysis procedures presented are also applied to actual [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) test-retest repeatability data for illustrative purposes.

Full Text

Duke Authors

Cited Authors

  • Huang, EP; Wang, X-F; Choudhury, KR; McShane, LM; Gönen, M; Ye, J; Buckler, AJ; Kinahan, PE; Reeves, AP; Jackson, EF; Guimaraes, AR; Zahlmann, G; Meta-Analysis Working Group,

Published Date

  • February 2015

Published In

Volume / Issue

  • 24 / 1

Start / End Page

  • 141 - 174

PubMed ID

  • 24872353

Pubmed Central ID

  • 24872353

Electronic International Standard Serial Number (EISSN)

  • 1477-0334

Digital Object Identifier (DOI)

  • 10.1177/0962280214537394

Language

  • eng

Conference Location

  • England