Quantitative Imaging Biomarkers: A Review of Statistical Methods for Technical Performance Assessment
By D. Raunig, Lisa M. McShane, Gene A Pennello, Constantine Gatsonis, P. Carson, J. Voyvodic, Richard L. Wahl, B. F. Kurland, Adam J Schwarz, M. Gönen and 7 more. Statistical Methods in Medical Research, 2014
358 citations recorded by the source corpus.
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.
Subfields: Radiology, Nuclear Medicine and Imaging, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/c890c337d1e06f32b7e969b600a410fd3c24c495
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Citations in the corpus
- Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom (2018, methodology): “The phantom was critical to the development and implementation of the QIBA DCE-MRI profile (3), which addresses all aspects of a DCE-MRI study, including data acquisition and processing.”
- AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, neutral): “QIBA and IBSI vision is to promote reproducible results emanating from imaging research methods by removing interoperability barriers and adopting software, hardware, and nomeclature standards and guidelines [63]–[66].”