Evidence map›Paper›PMID 34865129›Full record

ArticleNeuro-oncology2022

Diffusion MRI is an early biomarker of overall survival benefit in IDH wild-type recurrent glioblastoma treated with immune checkpoint inhibitors.

Akifumi Hagiwara, Talia C Oughourlian, Nicholas S Cho, Jacob Schlossman, Chencai Wang, Jingwen Yao, Catalina Raymond, Richard Everson, Kunal Patel, Sergey Mareninov and 8 more

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuro-oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 13% 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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

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  18. Depth of Radiographic Response and Time to Tumor Regrowth Predicts Overall Survival Following Anti-VEGF Therapy in Recurrent Glioblastoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023
    Article
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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

18 authors at 1 institution in 2 countries.

Akifumi HagiwaraUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA.ORCID 0000-0001-5277-3249
Talia C OughourlianUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA.
Nicholas S ChoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA.
Jacob SchlossmanUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA.
Chencai WangUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA.
Jingwen YaoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA.
Catalina RaymondUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA.
Richard EversonDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Kunal PatelDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Sergey MareninovDepartment of Pathology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Fausto J RodriguezDepartment of Pathology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Noriko SalamonDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Whitney B PopeDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Phioanh L NghiemphuUCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, California, USA.
Linda M LiauDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.ORCID 0000-0002-4053-0052
Robert M PrinsDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Timothy F CloughesyUCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, California, USA.ORCID 0000-0002-8656-7483
Benjamin M EllingsonUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA.ORCID 0000-0002-2764-6640
University of California, Los Angeles · US

Funding

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
Metabolic MRI for quantifying aerobic glycolysis and immune function in gliomasR21CA223757 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ELLINGSON, BENJAMIN M. · 2018 to 2019
$366k
EPA EP-C-15-003NCI NIH HHS P50 CA211015NCI NIH HHS R21 CA223757NIGMS NIH HHS T32 GM008042
6 · The paper itself

Abstract

backgroundDiffusion MRI estimates of the apparent diffusion coefficient (ADC) have been shown to be useful in predicting treatment response in patients with glioblastoma (GBM), with ADC elevations indicating tumor cell death. We aimed to investigate whether the ADC values measured before and after treatment with immune checkpoint inhibitors (ICIs) and the changes in these ADC values could predict overall survival (OS) in patients with recurrent IDH wild-type GBM.

methodsForty-four patients who met the following inclusion criteria were included in this retrospective study: (i) diagnosed with recurrent IDH wild-type GBM and treated with either pembrolizumab or nivolumab and (ii) availability of diffusion data on pre- and post-ICI MRI. Tumor volume and the median relative ADC (rADC) with respect to the normal-appearing white matter within the enhancing tumor were calculated.

resultsMedian OS among all patients was 8.1 months (range, 1.0-22.5 months). Log-rank test revealed that higher post-treatment rADC was associated with a significantly longer OS (median, 10.3 months for rADC ≥ 1.63 versus 6.1 months for rADC < 1.63; P = .02), whereas tumor volume, pretreatment rADC, and changes in rADC after treatment were not significantly associated with OS. Cox regression analysis revealed that post-treatment rADC significantly influenced OS (P = .02, univariate analysis), even after controlling for age and sex (P =.01, multivariate analysis), and additionally controlling for surgery after ICI treatment (P = .045, multivariate analysis).

conclusionsElevated post-treatment rADC may be an early imaging biomarker for OS benefits in GBM patients receiving ICI treatment.

Indexed as

Brain NeoplasmsGlioblastomaBiomarkersDiffusion Magnetic Resonance ImagingHumansImmune Checkpoint InhibitorsRetrospective StudiesBiomarkersImmune Checkpoint InhibitorsADCICIIDH wild typeMRIrecurrent glioblastoma

Identifiers

PMID34865129
PMCPMC9159421
OpenAlexW4200269953

What OpenQuestion holds

Textmetadata
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.