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A Bayesian framework for performance assessment and comparison of imaging biomarker quantification methods.

Brian J Smith1, Reinhard R Beichel2,3

  • 11 Department of Biostatistics, University of Iowa, Iowa City, IA, USA.

Statistical Methods in Medical Research
|December 23, 2017
PubMed
Summary

A new Bayesian framework helps assess bias and variability in quantitative imaging biomarkers. This approach aids in comparing methods and estimating sample sizes for clinical studies, improving diagnostic accuracy and treatment guidance.

Keywords:
BayesianQuantitative imaging biomarkersagreementbiasprecisionsample size

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Area of Science:

  • Medical Imaging
  • Biomarker Quantification
  • Statistical Modeling

Background:

  • Quantitative imaging biomarkers are crucial for disease diagnosis, treatment guidance, and outcome prediction.
  • Variability in imaging biomarkers arises from scanner technologies, region identification, and algorithms, hindering multi-institutional standards.

Purpose of the Study:

  • To present a Bayesian framework for assessing bias and variability in imaging biomarkers.
  • To compare quantification methods against a reference standard and evaluate prognostic performance.
  • To estimate sample size for future clinical studies using imaging biomarkers.

Main Methods:

  • Developed a Bayesian framework for bias and variability assessment.
  • Applied the framework to positron emission tomography (PET) data from a challenge study.
  • Utilized manual and semi-automated segmentation algorithms for tumor volume measurement.

Main Results:

  • Provided estimates and comparisons of bias and variability in imaging biomarker measurements.
  • Demonstrated the framework's utility in a real-world PET imaging challenge.
  • Developed an R software package and an online web application for analysis.

Conclusions:

  • The Bayesian framework offers a robust method for evaluating quantitative imaging biomarker reliability.
  • This approach facilitates the development of multi-institutional standards for biomarker quantification.
  • The accompanying software and web application support technical performance and sample size analysis for clinical studies.