Multiparameter quantitative imaging incorporates anatomical, functional, and/or behavioral
biomarkers to characterize tissue, detect disease, identify phenotypes, define longitudinal
change, or predict outcome. Multiple imaging parameters are sometimes considered separately
but ideally are evaluated collectively. Often, they are transformed as Likert interpretations,
ignoring the correlations of quantitative properties that may result in better reproducibility
or outcome prediction. In this paper we present three use cases of multiparameter
quantitative imaging: i) multidimensional descriptor, ii) phenotype classification,
and iii) risk prediction. A fourth application based on data-driven markers from radiomics
is also presented. We describe the technical performance characteristics and their
metrics common to all use cases, and provide a structure for the development, estimation,
and testing of multiparameter quantitative imaging. This paper serves as an overview
for a series of individual articles on the four applications, providing the statistical
framework for multiparameter imaging applications in medicine.
Key Words
Abbreviations:
QIBA (Quantitative Imaging Biomarker Alliance), QIB (Quantitative Imaging Biomarker)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: September 27, 2022
Accepted:
August 26,
2022
Received in revised form:
August 19,
2022
Received:
June 2,
2022
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
