Evidence map›Paper›PMID 42745091›Full record

ArticleJournal of neuro-oncology2026

MRI-based radiomics for prognosis of non-midline, pediatric high-grade gliomas.

Mariam Tolba, Michael Zhang, Jonathan K Duh, Linda Liverani, Julia Chang, Nucharin Supakul, Robert M Lober, Samuel H Cheshier, Sarah A Mattonen, Alok Jaju and 2 more

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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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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

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Mariam Tolba *UMass Chan Medical School, Worcester, MA, USA.
Michael Zhang *Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
Jonathan K DuhDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
Linda LiveraniDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA. liverani@stanford.edu.ORCID https://orcid.org/0000-0002-5210-3357
Julia ChangDepartment of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.
Nucharin SupakulDepartments of Clinical Radiology & Imaging Sciences, Riley Children's Hospital, Indiana University, Indianapolis, IN, USA.
Robert M LoberDepartment of Neurosurgery, Dayton Children's Hospital, Wright State University, Boonshoft School of Medicine, Dayton, OH, USA.
Samuel H CheshierDepartment of Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT, USA.
Sarah A MattonenDepartment of Medical Biophysics, Western University, London, ON, Canada.
Alok JajuDepartment of Medical Imaging, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.
Laura M ProloDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
Kristen W YeomDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA. kyeom@stanford.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeNon-midline, pediatric high-grade gliomas (HGG) represent one of the leading causes of cancer-related deaths in children with underlying molecular genetics and driver mutations that are distinct from adult HGGs. Image-based biomarkers that assist pediatric HGG risk-stratification could potentiate treatment strategies. Towards this, we investigated radiomics approaches for non-midline pediatric HGG prognosis.

methodsWe identified 77 children (mean age: 140 months; 43 males) with non-midline-origin, hemispheric pediatric HGG tumors across five pediatric institutions. We extracted 1800 image-biomarker standardization initiative (IBSI)-based radiomics tumor features from treatment-naïve, axial gadolinium-enhanced axial T1- and axial T2-weighted brain MRI (Gad T1-MRI, T2-MRI). We performed k-fold cross validation and Cox regression to identify optimal predictive features of overall survival. We calculated the risk scores for each patient using a linear combination of the selected features weighted by their coefficients determined by the Cox regression model. Patients were stratified based on the median risk score into high- and low-risk groups. Python (version 3.10.0) was used for all model development.

resultsThe Cox regression model that included both clinical (age and sex) and MRI-derived radiomics features demonstrated a concordance of 0.78 (95% CI: 0.70-0.83) compared to concordance of 0.75 (95% CI: 0.69-0.82) that used radiomics features alone and concordance of 0.61 (95% CI: 0.54-0.67) that used clinical features (age and sex) alone. Using the risk scores derived from our radiomics model, we present a Kaplan-Meier curve on our HGG cohort. Median overall survival was 21.7 months in the high-risk group and 44.6 months in the low-risk group (log-rank P = 0.007; hazard ratio 2.42, 95% CI 1.26-4.66).

conclusionIn this multi-center, pilot study, we identified optimal radiomics features in the creation of a prognostic pediatric HGG model. Computational MRI techniques may offer new approaches for quantitatively evaluating tumor phenotype and serve a potential future role in therapy planning and clinical trials eligibility.

Indexed as

Brain NeoplasmsGliomaMagnetic Resonance ImagingRadiomicsAdolescentChildChild, PreschoolFemaleFollow-Up StudiesHumansInfantMaleNeoplasm GradingPrognosisRetrospective StudiesCNS tumorsMachine learningNon midline gliomaPediatric high-grade gliomaPrognosisRadiomicsRisk-stratification

Identifiers

PMID42745091

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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.