Evidence map›Paper›PMID 32929644›Full record

ArticleJournal of neuro-oncology2020

Multiparametric MR-PET measurements in hypermetabolic regions reflect differences in molecular status and tumor grade in treatment-naïve diffuse gliomas.

Hiroyuki Tatekawa, Akifumi Hagiwara, Hiroyuki Uetani, Jingwen Yao, Talia C Oughourlian, Shadfar Bahri, Chencai Wang, Catalina Raymond, Albert Lai, Timothy F Cloughesy and 5 more

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

Article in Journal of neuro-oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 23% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Preferential tumor localization in relation toJournal of neuro-oncology · 2021
    Article
  6. Differentiating IDH status in human gliomas using machine learning and multiparametric MR/PET.Cancer imaging : the official publication of the International Cancer Imaging Society · 2021
    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

15 authors at 1 institution in 1 country.

Hiroyuki TatekawaUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd, Suite 615, Los Angeles, CA, 90024, USA.ORCID http://orcid.org/0000-0002-8050-4895
Akifumi HagiwaraUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd, Suite 615, Los Angeles, CA, 90024, USA.ORCID http://orcid.org/0000-0001-5277-3249
Hiroyuki UetaniDepartment of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90024, USA.ORCID http://orcid.org/0000-0002-0547-1210
Jingwen YaoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd, Suite 615, Los Angeles, CA, 90024, USA.ORCID http://orcid.org/0000-0002-8828-1841
Talia C OughourlianUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd, Suite 615, Los Angeles, CA, 90024, USA.ORCID http://orcid.org/0000-0001-8133-2928
Shadfar BahriDepartment of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA.ORCID http://orcid.org/0000-0002-2622-389X
Chencai WangUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd, Suite 615, Los Angeles, CA, 90024, USA.ORCID http://orcid.org/0000-0003-0501-3073
Catalina RaymondUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd, Suite 615, Los Angeles, CA, 90024, USA.ORCID http://orcid.org/0000-0003-2757-439X
Albert LaiUCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA.ORCID http://orcid.org/0000-0002-7265-2986
Timothy F CloughesyUCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA.ORCID http://orcid.org/0000-0002-8656-7483
Phioanh L NghiemphuUCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA.ORCID http://orcid.org/0000-0003-4064-6079
Linda M LiauUCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA.ORCID http://orcid.org/0000-0002-4053-0052
Whitney B PopeDepartment of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90024, USA.ORCID http://orcid.org/0000-0003-4803-1992
Noriko SalamonDepartment of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90024, USA.ORCID http://orcid.org/0000-0002-3520-9467
Benjamin M EllingsonUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd, Suite 615, Los Angeles, CA, 90024, USA. bellingson@mednet.ucla.edu.ORCID http://orcid.org/0000-0002-2764-6640
University of California, Los Angeles · US

Funding

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
American Brain Tumor Association ARC1700002American Cancer Society RSG-15-003-01-CCEEPA EP-C-15-003NCI NIH HHS P50 CA211015NCI NIH HHS R21 CA223757NIH/NCI 1R21CA223757-01NIH/NCI UCLA Brain Tumor SPORE 1P50CA211015-01A1
6 · The paper itself

Abstract

purposeTo assess whether hypermetabolically-defined regions of interest (ROIs) on 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) could be used to evaluate physiological features and whether there are measurable differences between molecular subtypes and tumor grades.

methodsSixty-eight treatment-naïve glioma patients who underwent FDOPA PET and magnetic resonance imaging (MRI) were retrospectively included. Fluid-attenuated inversion recovery hyperintense regions (FLAIR

resultsUsing either FLAIR

conclusionFDOPA hypermetabolic ROI may extract useful imaging features of gliomas, which can illuminate biological differences between different molecular status or tumor grades.

Indexed as

MutationAgedBiomarkers, TumorBrain NeoplasmsFemaleFollow-Up StudiesGliomaHumansIsocitrate DehydrogenaseMagnetic Resonance ImagingMaleMiddle AgedPositron-Emission TomographyPrognosisRetrospective StudiesROC CurveBiomarkers, TumorIsocitrate DehydrogenaseFDOPA PETGliomaHypermetabolic ROIMRI

Identifiers

PMID32929644
PMCPMC7682113
OpenAlexW3085424462

What OpenQuestion holds

Textmetadata
LicenceTDM
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.