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DOI: 10.1177/0962280214537344
¤ OpenAccess: Green
This work has “Green” OA status. This means it may cost money to access on the publisher landing page, but there is a free copy in an OA repository.

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

David 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 Kondratovich,Kevin O’Donnell,Nicholas Petrick,Patricia E. Cole,Brian S. Garra,Daniel C. Sullivan

Computer science
Biomarker
Imaging biomarker
2014
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.
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    Quantitative imaging biomarkers: A review of statistical methods for technical performance assessment” is a paper by David 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 Kondratovich Kevin O’Donnell Nicholas Petrick Patricia E. Cole Brian S. Garra Daniel C. Sullivan published in 2014. It has an Open Access status of “green”. You can read and download a PDF Full Text of this paper here.