Evidence map›Paper›PMID 36464548›Full record

ReviewAcademic radiology2023

Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.

David L Raunig, Gene A Pennello, Jana G Delfino, Andrew J Buckler, Timothy J Hall, Alexander R Guimaraes, Xiaofeng Wang, Erich P Huang, Huiman X Barnhart, Nandita deSouza and 1 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Academic radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

David L RaunigDepartment of Statistical and Quantitative Sciences, Data Science Institute, Takeda Pharmaceuticals, Cambridge, Massachusetts. Electronic address: draunig@snet.net.
Gene A PennelloCenter for Devices and Radiological Health, US Food and Drug Administration Division of Imaging, Diagnostic and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland.
Jana G DelfinoCenter for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland.
Andrew J BucklerElucid Bioimaging, Inc., Boston, Massachusetts.
Timothy J HallDepartment of Medical Physics, University of Wisconsin, Madison, Wisconsin.
Alexander R GuimaraesDepartment of Diagnostic Radiology, Oregon Health & Sciences University, Portland, Oregon.
Xiaofeng WangDepartment of Quantitative Health Sciences, Lerner Research Institute, Cleveland, Ohio.
Erich P HuangBiometric Research Program, Division of Cancer Treatment and Diagnosis - National Cancer Institute, National Institutes of Health, Bethesda, MD.
Huiman X BarnhartDepartment of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina.
Nandita deSouzaDivision of Radiotherapy and Imaging, the Insitute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom.
Nancy ObuchowskiDepartment of Quantitative Health Sciences, Lerner Research Institute Cleveland Clinic Foundation, Cleveland, Ohio.

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Intramural FDA HHS FD999999Intramural NIH HHS Z99 CA999999NIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.

Indexed as

Alzheimer DiseaseDiagnostic ImagingBiomarkersHumansBiomarkersAlzheimer's DiseaseMultiparametric quantitative imaging biomarker (mp-QIB)Multivariate biomarkerQIBATechnical performance

Identifiers

PMID36464548
PMCPMC9825667

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.