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Best Practice for MRI Diagnostic Accuracy Research With Lessons and Examples from the LI-RADS Individual Participant Data Group.
van der Pol, Christian B; Costa, Andreu F; Lam, Eric; Dawit, Haben; Bashir, Mustafa R; McInnes, Matthew D F.
  • van der Pol CB; Department of Diagnostic Imaging, Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, Hamilton, Ontario, Canada.
  • Costa AF; McMaster University, Hamilton, Ontario, Canada.
  • Lam E; Department of Diagnostic Radiology, Queen Elizabeth II Health Sciences Centre and Dalhousie University, Halifax, Nova Scotia, Canada.
  • Dawit H; Ottawa Hospital Research Institute Clinical Epidemiology Program, Ottawa, Ontario, Canada.
  • Bashir MR; Ottawa Hospital Research Institute Clinical Epidemiology Program, Ottawa, Ontario, Canada.
  • McInnes MDF; Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
J Magn Reson Imaging ; 2023 Oct 11.
Article en En | MEDLINE | ID: mdl-37818955
ABSTRACT
Medical imaging diagnostic test accuracy research is strengthened by adhering to best practices for study design, data collection, data documentation, and study reporting. In this review, key elements of such research are discussed, and specific recommendations provided for optimizing diagnostic accuracy study execution to improve uniformity, minimize common sources of bias and avoid potential pitfalls. Examples are provided regarding study methodology and data collection practices based on insights gained by the liver imaging reporting and data system (LI-RADS) individual participant data group, who have evaluated raw data from numerous MRI diagnostic accuracy studies for risk of bias and data integrity. The goal of this review is to outline strategies for investigators to improve research practices, and to help reviewers and readers better contextualize a study's findings while understanding its limitations. LEVEL OF EVIDENCE 5 TECHNICAL EFFICACY Stage 3.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Guideline Idioma: En Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Guideline Idioma: En Año: 2023 Tipo del documento: Article