Evidence map›Paper›PMID 42811203›Full record

ArticleEuropean radiology2026

Imaging patterns of necrosis after treatment with chemoradiation are prognostic for survival in glioblastoma.

Johanna Heugenhauser, Francesco Sanvito, Jingwen Yao, Irina Kryukov, Catalina Raymond, Ashley Teraishi, Phioanh L Nghiemphu, Albert Lai, Robert M Prins, David A Nathanson and 2 more

Abstract read
PubMed Publisher
In one paragraph

Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

12 authors.

Johanna Heugenhauser *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.
Irina KryukovUCLA 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.
Ashley TeraishiUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Phioanh L NghiemphuDepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Albert LaiDepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Robert M PrinsDepartment of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
David A NathansonDepartment of Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Timothy F CloughesyDepartment of Neurology, David Geffen School of Medicine, 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, Los Angeles, CA, USA. bellingson@mednet.ucla.edu.ORCID http://orcid.org/0000-0002-2764-6640

Funding

NCI NIH HHS NIH NCI P50 CA211015NCI NIH HHS NIH NCI R01CA270027NCI NIH HHS NIH NCI R01CA279984U.S. Department of Defense DoD CA20029U.S. Department of Defense DoD CA220732
6 · The paper itself

Abstract

objectivesTumor necrosis is a hallmark of glioblastoma. While recent data suggest that necrotic tissue plays an active role in glioblastoma growth, the prognostic relevance of necrosis imaging remains understudied. This study evaluated necrotic tissue on MRI to identify a prognostic morphological signature and characterize its underlying biological characteristics. MATERIALS AND

methods82 patients with glioblastoma imaged after chemoradiation were retrospectively analyzed. A combination of two shape features (volume density and surface-to-volume ratio) was used to define prognostic necrosis phenotypes (low- vs. high-risk). The association between necrosis phenotypes and survival was tested with log-rank and multivariable Cox models adjusting for other prognostic variables. Diffusion, perfusion, and pH-weighted MRI were used to biologically characterize the two groups.

resultsThe low-risk necrosis phenotype was more compact (not spatially dispersed), with smoother margins and fewer fragmentations. Patients with this phenotype had an almost three-fold lower hazard of death (HR: 0.35, p = 0.001), which was confirmed after adjusting for tumor volume, MGMT status, age, and Karnofsky performance score (KPS) (HR: 0.29, p = 0.001). Low-risk necrosis was characterized by greater necrosis volume and necrosis proportion, fewer isolated clusters, lower perfusion values, and higher oxygen extraction values.

conclusionsThe necrosis morphology bears prognostic information in post-chemoradiation glioblastoma and exhibits distinct biological features. KEY POINTS: Question Are quantitative MRI-derived necrosis phenotypes associated with survival in post-chemoradiation glioblastoma? Findings The low-risk necrosis phenotype, derived from two quantitative necrosis features, is independently associated with patient survival and reflects distinct perfusion characteristics. Clinical relevance Quantitative assessment of necrosis morphology from routine MRI may complement established prognostic factors and enable improved risk stratification of patients with glioblastoma after chemoradiation.

Indexed as

GlioblastomaMolecular imagingNecrosisPerfusion imagingRadiomics

Identifiers

PMID42811203

What OpenQuestion holds

Textmetadata
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.