Journal article
A Bayesian framework for performance assessment and comparison of imaging biomarker quantification methods
Statistical methods in medical research, Vol.28(4), pp.1003-1018
04/2019
DOI: 10.1177/0962280217741334
PMCID: PMC6045465
PMID: 29271301
Abstract
Quantitative biomarkers derived from medical images are being used increasingly to help diagnose disease, guide treatment, and predict clinical outcomes. Measurement of quantitative imaging biomarkers is subject to bias and variability from multiple sources, including the scanner technologies that produce images, the approaches for identifying regions of interest in images, and the algorithms that calculate biomarkers from regions. Moreover, these sources may differ within and between the quantification methods employed by institutions, thus making it difficult to develop and implement multi-institutional standards. We present a Bayesian framework for assessing bias and variability in imaging biomarkers derived from different quantification methods, comparing agreement to a reference standard, studying prognostic performance, and estimating sample size for future clinical studies. The statistical methods are illustrated with data obtained from a positron emission tomography challenge conducted by members of the NCI's Quantitative Imaging Network program, in which tumor volumes were measured manually and with seven different semi-automated segmentation algorithms. Estimates and comparisons of bias and variability in the resulting measurements are provided along with an R software package for the technical performance analysis and an online web application for sample size and power analysis.
Details
- Title: Subtitle
- A Bayesian framework for performance assessment and comparison of imaging biomarker quantification methods
- Creators
- Brian J SmithReinhard R Beichel
- Resource Type
- Journal article
- Publication Details
- Statistical methods in medical research, Vol.28(4), pp.1003-1018
- Publisher
- SAGE Publications; London, England
- DOI
- 10.1177/0962280217741334
- PMID
- 29271301
- PMCID
- PMC6045465
- ISSN
- 0962-2802
- eISSN
- 1477-0334
- Language
- English
- Date published
- 04/2019
- Academic Unit
- Electrical and Computer Engineering; Biostatistics; Holden Comprehensive Cancer Center
- Record Identifier
- 9983997450402771
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