Evidence map›Paper›PMID 36452056›Full record

ReviewBJR open2022

Statistical considerations for repeatability and reproducibility of quantitative imaging biomarkers.

Shangyuan Ye, Jeong Youn Lim, Wei Huang

Open access · goldAbstract readReview
In one paragraph

Review in BJR open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.3field-weighted citation impact, top 21% of its field
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

9 citing papers in PubMed, 11 citations in OpenAlex.

  1. Trial
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  9. [ROC curves: general characteristics and their usefulness in clinical practice].Revista medica del Instituto Mexicano del Seguro Social · 2023
    Article
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

3 authors at 2 institutions in 1 country.

Shangyuan YeBiostatistics Shared Resource, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States.
Jeong Youn LimBiostatistics Shared Resource, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States.
Wei HuangAdvanced Imaging Research Center, Oregon Health and Science University, Portland, OR, United States.
Oregon Health & Science University · USAdvanced Imaging Research (United States) · US

Funding

Multicenter Quantitative MRI Assessment of Breast Cancer Therapy ResponseR01CA248192 · NCI · WILLIAM BEAUMONT HOSPITAL RESEARCH INST · PI WEI HUANG, James H Holmes · 2021 to 2026
$2.8M
NCI NIH HHS R01 CA248192
6 · The paper itself

Abstract

Quantitative imaging biomarkers (QIBs) are increasingly used in clinical studies. Because many QIBs are derived through multiple steps in image data acquisition and data analysis, QIB measurements can produce large variabilities, posing a significant challenge in translating QIBs into clinical trials, and ultimately, clinical practice. Both repeatability and reproducibility constitute the reliability of a QIB measurement. In this article, we review the statistical aspects of repeatability and reproducibility of QIB measurements by introducing methods and metrics for assessments of QIB repeatability and reproducibility and illustrating the impact of QIB measurement error on sample size and statistical power calculations, as well as predictive performance with a QIB as a predictive biomarker.

Identifiers

PMID36452056
PMCPMC9667479
OpenAlexW4289518895

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.