Evidence map›Paper›PMID 41563605›Full record

ArticleJournal of neuro-oncology2026

Magnetic resonance imaging features differentiate histologic and molecular subtypes of glioblastoma IDH-Wild type CNS WHO grade 4.

Sohil H Patel, Shanna Mayorov, Wooil Kim, Kanwar Singh, James R Loftus, James T Patrie, Prem P Batchala, Allen Ko, Matthew D Lee, Rajan Jain and 1 more

Abstract read
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Article in Journal of neuro-oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Sohil H PatelDepartment of Radiology and Imaging Sciences, Indiana University Health, 550 N. University Blvd. Indianapolis, Indianapolis, IN, 46202, USA. spatel63@iuhealth.org.ORCID http://orcid.org/0000-0002-9739-4362
Shanna MayorovDepartment of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA, USA.
Wooil KimDepartment of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA, USA.
Kanwar SinghDepartment of Radiology, New York University School of Medicine, 550 1st Avenue, New York, NY, 10016, USA.
James R LoftusDepartment of Radiology, New York University School of Medicine, 550 1st Avenue, New York, NY, 10016, USA.
James T PatrieDepartment of Public Health Sciences, University of Virginia Health System, Charlottesville, VA, USA.
Prem P BatchalaDepartment of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA, USA.
Allen KoDepartment of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA, USA.
Matthew D LeeDepartment of Radiology, New York University School of Medicine, 550 1st Avenue, New York, NY, 10016, USA.
Rajan JainDepartment of Radiology, New York University School of Medicine, 550 1st Avenue, New York, NY, 10016, USA.
David SchiffDivision of Neuro-Oncology, University of Virginia Health System, Charlottesville, VA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeGlioblastoma IDH-wild type, CNS WHO grade 4 (GBM) can be diagnosed on the basis of histologic features (histological-GBM) or molecular features (molecular-GBM). Only few studies report neuroimaging features of GBM in its modern classification, and none have controlled for surgical status or used multiple logistic regression analysis to determine unique predictors. Our study aimed to validate MRI features that distinguish histological-GBM and molecular-GBM.

methodsWe analyzed a training cohort (n = 255) and validation cohort (n = 44) of GBM cases, classified according to the 2021 WHO Classification of Tumors of the CNS. For the training cohort, univariate and multiple logistic regression analyses determined if MRI metrics (contrast enhancement, ring-enhancement, vasogenic edema, multifocal tumor, lesion diameter, hemorrhage, number of lobes, and normalized ADC) and surgery type (biopsy vs. resection) predicted GBM-type (histological vs. molecular). A reduced multiple logistic regression model was constructed and applied to the validation dataset.

resultsThere were 231 histological-GBMs and 24 molecular-GBMs in the training cohort. Multiple logistic regression analysis including both MRI metrics and surgery type showed that contrast enhancement (OR 7.83 [95%CI: 1.23–49.68], p = 0.029), ring enhancement (OR 5.98 [95%CI: 1.09–32.93, p = 0.040), and normalized ADC (OR 0.78 [95%CI: 0.62–0.99], p = 0.039) differed between histological and molecular-GBM. Analysis of the validation dataset using the unique training dataset-derived predictor variables (contrast-enhancement, ring-enhancement, and normalized ADC) found correct classification of each histological and molecular-GBM.

conclusionMolecular and histological-GBM exhibit distinct MRI phenotypes independent of surgical status.

Indexed as

Brain NeoplasmsGlioblastomaIsocitrate DehydrogenaseMagnetic Resonance ImagingAdultAgedFemaleHumansMaleMiddle AgedNeoplasm GradingWorld Health OrganizationIsocitrate DehydrogenaseGlioblastomaGliomaIsocitrate Dehydrogenase (IDH)MRIWorld Health Organization (WHO)

Identifiers

PMID41563605
PMCPMC12823664

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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.