Evidence map›Paper›PMID 41717139›Full record

ArticleBrain communications2026

Data-driven probabilistic mapping of the spatial and molecular landscape of glioma.

Nardin Samuel, Jurgen Germann, Andrew Yang, Can Sarica, Alexandre Boutet, Artur Vetkas, Brendan Santyr, Stefan Lang, Davide Chicco, Andres M Lozano

Abstract read
In one paragraph

Article in Brain communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. An MRI Atlas of Regional Brain Vulnerability to Metastatic Disease.medRxiv : the preprint server for health sciences · 2026
    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

10 authors.

Nardin SamuelDivision of Neurology, Department of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada.
Jurgen GermannKrembil Brain Institute, University Health Network, Toronto, ON M5G 2C4, Canada.ORCID https://orcid.org/0000-0003-0995-8226
Andrew YangKrembil Brain Institute, University Health Network, Toronto, ON M5G 2C4, Canada.
Can SaricaKrembil Brain Institute, University Health Network, Toronto, ON M5G 2C4, Canada.
Alexandre BoutetDepartment of Radiology, University of Toronto, Toronto, ON M5G 2C4, Canada.ORCID https://orcid.org/0000-0001-6942-5195
Artur VetkasDepartment of Neurosurgery, Tartu University Hospital, Tartu 50406, Estonia.ORCID https://orcid.org/0000-0002-4487-432X
Brendan SantyrDivision of Neurosurgery, Toronto Western Hospital, Toronto, ON M5T 2S8, Canada.ORCID https://orcid.org/0000-0003-1782-4527
Stefan LangDivision of Neurosurgery, Department of Surgery, University of British Columbia, Vancouver, BC V5Z 1M9, Canada.
Davide ChiccoDipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca, Milan 20126, Italy.ORCID https://orcid.org/0000-0001-9655-7142
Andres M LozanoKrembil Brain Institute, University Health Network, Toronto, ON M5G 2C4, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the spatial distribution of gliomas in the brain and their molecular subtypes can aid in the diagnosis and development of targeted therapies. This study aims to create probabilistic radiologic maps of glioma locations using large MRI datasets and the most recent consensus brain tumour classification. Neuroimaging data from multiple databases were analysed. Patients included had MRI T1 images and validated tumour segmentations. Probabilistic tumour maps were generated whereby binary tumour masks were aligned to a standard brain template and aggregated to compute voxel-wise frequency maps of glioma occurrence detailing glioma volume, molecular subtype, age, sex and overall survival with tumour location. The study included 2164 patients with gliomas. Key findings include distinct spatial patterns associated with glioma size and molecular subtype: smaller tumours favoured the left temporal region, medium-sized tumours the medial frontoparietal and bilateral temporal regions and larger tumours the frontotemporoparietal regions, predominantly on the right. Isocitrate dehydrogenase (IDH)-wild-type tumours were more common in medial parietotemporal regions, while IDH-mutant tumours were preferentially found in frontotemporal regions. Younger patients had more frontal tumours, while older patients had higher parieto-occipital tumour burdens. Tumours in medial structures and parietal lobes were linked to lower survival, whereas right temporal tumours were associated with higher rates of survival. These findings likely correlate with IDH mutation status. Leveraging eight glioma databases, probabilistic tumour maps revealed significant relationships between brain regions, molecular subtypes and clinical outcomes. These findings could be used in clinical decision-making and offer insights into glioma pathogenesis and treatment of patients impacted by this disease.

Indexed as

gliomaprobabilistic mappingradiology

Identifiers

PMID41717139
PMCPMC12914576

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.