Quantitative imaging biomarkers: A review of statistical methods for technical performance assessment

David L. Raunig, Lisa M. McShane, Gene Pennello, Constantine Gatsonis, Paul L. Carson, James T. Voyvodic, Richard L. Wahl, Brenda F. Kurland, Adam J. Schwarz, Mithat Gönen, Gudrun Zahlmann, Marina V. Kondratovich, Kevin O'Donnell, Nicholas Petrick, Patricia E. Cole, Brian Garra, Daniel C. Sullivan

Research output: Contribution to journalReview articlepeer-review

155 Scopus citations


Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers to measure changes in these features. Critical to the performance of a quantitative imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a quantitative imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of quantitative imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined.

Original languageEnglish (US)
Pages (from-to)27-67
Number of pages41
JournalStatistical Methods in Medical Research
Issue number1
StatePublished - Feb 27 2015


  • agreement
  • bias
  • imaging biomarkers
  • linearity
  • precision
  • quantitative imaging
  • reliability
  • repeatability
  • reproducibility

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management


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