Evidence map›Paper›PMID 35058510›Full record

ArticleScientific reports2022

Visualization of tumor heterogeneity and prediction of isocitrate dehydrogenase mutation status for human gliomas using multiparametric physiologic and metabolic MRI.

Akifumi Hagiwara, Hiroyuki Tatekawa, Jingwen Yao, Catalina Raymond, Richard Everson, Kunal Patel, Sergey Mareninov, William H Yong, Noriko Salamon, Whitney B Pope and 4 more

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

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.8field-weighted citation impact, top 9% 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

15 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. MRI-based habitat imaging predicts high-risk molecular subtypes and early risk assessment of lower-grade gliomas.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. Article
  12. Review
  13. Review
  14. Updates in IDH-Wildtype Glioblastoma.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2022
    Review
  15. 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

14 authors at 3 institutions in 2 countries.

Akifumi HagiwaraUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
Hiroyuki TatekawaUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
Jingwen YaoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
Catalina RaymondUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
Richard EversonDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Kunal PatelDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Sergey MareninovDepartment of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
William H YongDepartment of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Noriko SalamonDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Whitney B PopeDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Phioanh L NghiemphuUCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA.
Linda M LiauDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Timothy F CloughesyUCLA Neuro-Oncology Program, 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, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA. bellingson@mednet.ucla.edu.
University of California, Los Angeles · USOsaka City University · JPSamueli Institute · US

Funding

Women's CancersP30CA016042 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Samuel Wheeler French · 1985 to 2026
$134.5M
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 P30 CA016042NCI NIH HHS P50 CA211015NCI NIH HHS R21 CA223757NIH HHS NIH/NCI 1R21CA223757-01
6 · The paper itself

Abstract

This study aimed to differentiate isocitrate dehydrogenase (IDH) mutation status with the voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and to discover biological underpinnings of the clusters. A total of 69 patients with treatment-naïve diffuse glioma were scanned with pH-sensitive amine chemical exchange saturation transfer MRI, diffusion-weighted imaging, fluid-attenuated inversion recovery, and contrast-enhanced T1-weighted imaging at 3 T. An unsupervised two-level clustering approach was used for feature extraction from acquired images. The logarithmic ratio of the labels in each class within tumor regions was applied to a support vector machine to differentiate IDH status. The highest performance to predict IDH mutation status was found for 10-class clustering, with a mean area under the curve, accuracy, sensitivity, and specificity of 0.94, 0.91, 0.90, and 0.91, respectively. Targeted biopsies revealed that the tissues with labels 7-10 showed high expression levels of hypoxia-inducible factor 1-alpha, glucose transporter 3, and hexokinase 2, which are typical of IDH wild-type glioma, whereas those with labels 1 showed low expression of these proteins. In conclusion, A machine learning model successfully predicted the IDH mutation status of gliomas, and the resulting clusters properly reflected the metabolic status of the tumors.

Indexed as

AdultAgedAged, 80 and overBiomarkers, TumorBrain NeoplasmsCluster AnalysisFemaleGliomaHumansImage Processing, Computer-AssistedIsocitrate DehydrogenaseMachine LearningMaleMiddle AgedMultiparametric Magnetic Resonance ImagingMutationBiomarkers, TumorIDH1 protein, humanIsocitrate Dehydrogenase

Identifiers

PMID35058510
PMCPMC8776874
OpenAlexW4205505760

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.