Evidence map›Paper›PMID 40067460›Full record

ArticleNeuroradiology2025

Normalization of the percentage of signal recovery derived from dynamic susceptibility contrast perfusion MRI in brain tumors.

Francesco Sanvito, Jingwen Yao, Nicholas S Cho, Donatello Telesca, Noriko Salamon, Timothy F Cloughesy, Benjamin M Ellingson

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Francesco SanvitoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Jingwen YaoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Nicholas S ChoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Donatello TelescaDepartment of Biostatistics, University of California, Los Angeles, USA.
Noriko SalamonDepartment of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Timothy F CloughesyDepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Benjamin M EllingsonUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu.ORCID http://orcid.org/0000-0002-2764-6640

Funding

Women's CancersP30CA016042 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Samuel Wheeler French · 1985 to 2026
$134.5M
UCLA-Caltech Medical Scientist Training ProgramT32GM008042 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AJIJOLA, OLUJIMI A, DAWSON, DAVID WAYNE · 1985 to 2023
$29.9M
UCLA SPORE in Brain CancerP50CA211015 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Yvonne Yu-Hsuan Chen · 2017 to 2026
$25.2M
Role of decorin and diffusion MRI in anti-VEGF efficacy for recurrent glioblastomaR01CA270027 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI TIMOTHY CLOUGHESY, Benjamin M. Ellingson · 2022 to 2026
$3.0M
Quantitative molecular MR-PET imaging of glycolysis in glioblastomaR01CA279984 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Benjamin M. Ellingson, David A. Nathanson · 2023 to 2026
$2.4M
Molecular MR-PET to characterize malignant transformation and identify therapeutic vulnerabilities in human IDH-mutant gliomasF30CA284809 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHO, NICHOLAS S. · 2023 to 2025
$136k
Foundation for the National Institutes of Health NIH NCI F30CA284809Foundation for the National Institutes of Health NIH NCI R01CA270027Foundation for the National Institutes of Health NIH NCI R01CA279984Foundation for the National Institutes of Health NIH NIGMS T32GM008042NCI NIH HHS F30 CA284809NCI NIH HHS P30 CA016042NCI NIH HHS P50 CA211015NCI NIH HHS R01 CA270027NCI NIH HHS R01 CA279984NIGMS NIH HHS T32 GM008042U.S. Department of Defense DoD CA20029U.S. Department of Defense DoD CA220732
6 · The paper itself

Abstract

The universalizability of the metric percentage of signal recovery (PSR) derived from dynamic susceptibility contrast (DSC) perfusion MRI is limited by its dependency of acquisition parameters. In this technical assessment, we tested different reference tissues for PSR normalization and found the normal-appearing white matter (NAWM) to have the least inter-patient variability when using a fixed PSR-optimized protocol. A logarithmic normalization using NAWM improved the consistency of PSR values when a cohort of brain tumor patients was analyzed by synthetically changing acquisition parameters (while keeping the protocol uniform within the cohort). Additionally, the NAWM logarithmic normalization was better than no normalization and linear normalization at maintaining the consistency of both values and ranks within the cohort while a synthetic random variation of the acquisition parameter was applied (i.e., with a heterogeneous protocol within the cohort). Future PSR studies may consider reporting logarithmic normalized PSR (nPSR

Indexed as

Brain NeoplasmsImage Interpretation, Computer-AssistedPerfusion Magnetic Resonance ImagingAdultAgedContrast MediaFemaleHumansMaleMiddle AgedContrast MediaBrain tumorsGlioblastomaNormalizationPercentage of signal recoveryPerfusion imaging

Identifiers

PMID40067460
PMCPMC12357792

What OpenQuestion holds

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LicenceCC BY
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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.