Literature DB >> 24919831

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

David L Raunig1, Lisa M McShane2, Gene Pennello3, Constantine Gatsonis4, Paul L Carson5, James T Voyvodic6, Richard L Wahl7, Brenda F Kurland8, Adam J Schwarz9, Mithat Gönen10, Gudrun Zahlmann11, Marina V Kondratovich3, Kevin O'Donnell12, Nicholas Petrick3, Patricia E Cole13, Brian Garra3, Daniel C Sullivan14.   

Abstract

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.
© The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

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Keywords:  agreement; bias; imaging biomarkers; linearity; precision; quantitative imaging; reliability; repeatability; reproducibility

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Year:  2014        PMID: 24919831      PMCID: PMC5574197          DOI: 10.1177/0962280214537344

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  21 in total

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Review 10.  Quantitative imaging biomarkers: a review of statistical methods for computer algorithm comparisons.

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Journal:  Stat Methods Med Res       Date:  2014-06-11       Impact factor: 3.021

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  119 in total

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5.  Inter- and intra-software reproducibility of computed tomography lung density measurements.

Authors:  Miranda Kirby; Charles Hatt; Nancy Obuchowski; Stephen M Humphries; Jered Sieren; David A Lynch; Sean B Fain
Journal:  Med Phys       Date:  2020-03-31       Impact factor: 4.071

6.  Investigating the Robustness Neighborhood Gray Tone Difference Matrix and Gray Level Co-occurrence Matrix Radiomic Features on Clinical Computed Tomography Systems Using Anthropomorphic Phantoms: Evidence From a Multivendor Study.

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7.  Statistical analysis of lung nodule volume measurements with CT in a large-scale phantom study.

Authors:  Qin Li; Marios A Gavrielides; Berkman Sahiner; Kyle J Myers; Rongping Zeng; Nicholas Petrick
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8.  Repeatability and reproducibility of ADC measurements: a prospective multicenter whole-body-MRI study.

Authors:  Nicolas F Michoux; Jakub W Ceranka; Jef Vandemeulebroucke; Frank Peeters; Pierre Lu; Julie Absil; Perrine Triqueneaux; Yan Liu; Laurence Collette; Inneke Willekens; Carola Brussaard; Olivier Debeir; Stephan Hahn; Hubert Raeymaekers; Johan de Mey; Thierry Metens; Frédéric E Lecouvet
Journal:  Eur Radiol       Date:  2021-01-06       Impact factor: 5.315

Review 9.  Methods and challenges in quantitative imaging biomarker development.

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Journal:  Acad Radiol       Date:  2015-01       Impact factor: 3.173

Review 10.  MR Imaging Biomarkers in Oncology Clinical Trials.

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Journal:  Magn Reson Imaging Clin N Am       Date:  2016-02       Impact factor: 2.266

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