Evidence map›Paper›PMID 40542949›Full record

ArticleJournal of neuro-oncology2025

Independent histological validation of MR-derived radio-pathomic maps of tumor cell density using image-guided biopsies in human brain tumors.

Gianluca Nocera, Francesco Sanvito, Jingwen Yao, Sonoko Oshima, Samuel A Bobholz, Ashley Teraishi, Catalina Raymond, Kunal Patel, Richard G Everson, Linda M Liau and 7 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neuro-oncology, 2025. 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. 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

17 authors.

Gianluca Nocera *UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Francesco Sanvito *UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Jingwen YaoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Sonoko OshimaUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Samuel A BobholzDepartment of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA.
Ashley TeraishiUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Catalina RaymondUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Kunal PatelDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Richard G EversonDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Linda M LiauDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Jennifer ConnellyDepartment of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA.
Antonella CastellanoUniversity Vita-Salute San Raffaele, Milan, Italy.
Pietro MortiniUniversity Vita-Salute San Raffaele, Milan, Italy.
Noriko SalamonDepartment of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Timothy F CloughesyDepartment of Neurology, Ronald Reagan UCLA Medical Center, University of California, Los Angeles, CA, USA.
Peter S LaVioletteDepartment of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA.
Benjamin M EllingsonUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA. bellingson@mednet.ucla.edu.

Funding

UCLA SPORE in Brain CancerP50CA211015 · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2025 to 2025
$2.2M
Prostate Cancer Radio-Pathomics for Differentiating Clinically Significant DiseaseR01CA249882 · NCI · MEDICAL COLLEGE OF WISCONSIN · 2021 to 2025
$1.2M
Radiopathomic Modeling of Glioma Heterogeneity Throughout a Patient's Disease TrajectoryR01CA290631 · MEDICAL COLLEGE OF WISCONSIN · 2025 to 2025
$678k
Role of decorin and diffusion MRI in anti-VEGF efficacy for recurrent glioblastomaR01CA270027 · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2025 to 2025
$608k
NCI NIH HHS P50 CA211015NCI NIH HHS R01 CA218144NCI NIH HHS R01 CA249882NCI NIH HHS R01 CA270027NCI NIH HHS R01 CA290631NIH HHS R01CA249882NIH HHS R01CA270027NIH HHS R01CA290631U.S. Department of Defense CDMRP CA220732
6 · The paper itself

Abstract

purposeIn brain gliomas, non-invasive biomarkers reflecting tumor cellularity would be useful to guide supramarginal resections and to plan stereotactic biopsies. We aim to validate a previously-trained machine learning algorithm that generates cellularity prediction maps (CPM) from multiparametric MRI data to an independent, retrospective external cohort of gliomas undergoing image-guided biopsies, and to compare the performance of CPM and diffusion MRI apparent diffusion coefficient (ADC) in predicting cellularity.

methodsA cohort of patients with treatment-naïve or recurrent gliomas were prospectively studied. All patients underwent pre-surgical MRI according to the standardized brain tumor imaging protocol. The surgical sampling site was planned based on image-guided biopsy targets and tissue was stained with hematoxylin-eosin for cell density count. The correlation between MRI-derived CPM values and histological cellularity, and between ADC and histological cellularity, was evaluated both assuming independent observations and accounting for non-independent observations.

resultsSixty-six samples from twenty-seven patients were collected. Thirteen patients had treatment-naïve tumors and fourteen had recurrent lesions. CPM value accurately predicted histological cellularity in treatment-naïve patients (b = 1.4, R

conclusionMRI-derived machine learning generated cellularity prediction maps (CPM) enabled a non-invasive evaluation of tumor cellularity in treatment-naïve glioma patients, although CPM did not clearly outperform ADC alone in this cohort.

Indexed as

Brain NeoplasmsGliomaImage-Guided BiopsyMagnetic Resonance ImagingAdultAgedCell CountDiffusion Magnetic Resonance ImagingFemaleHumansMachine LearningMaleMiddle AgedProspective StudiesRetrospective StudiesYoung AdultArtificial intelligenceDiffusion imagingGliomaHistological validationImaging biomarker

Identifiers

PMID40542949
PMCPMC12367939

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.