Evidence map›Paper›PMID 39899184›Full record

ArticleDiscover oncology2025

Unraveling the heterogeneity of WHO grade 4 gliomas: insights from clinical, imaging, and molecular characterization.

Haihui Jiang, Xijie Wang, Xiaodong Chen, Shouzan Zhang, Qingsen Ren, Mingxiao Li, Ming Li, Xiaohui Ren, Song Lin, Yong Cui

Abstract read
In one paragraph

Article in Discover 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

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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

1 citing paper in PubMed.

  1. Article
4 · The record

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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.

Haihui Jiang *Department of Neurosurgery, Peking University Third Hospital, Peking University, Beijing, China.
Xijie Wang *Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Xiaodong Chen *Department of Neurosurgery, Peking University Third Hospital, Peking University, Beijing, China.
Shouzan ZhangDepartment of Neurosurgery, Peking University Third Hospital, Peking University, Beijing, China.
Qingsen RenDepartment of Neurosurgery, Peking University Third Hospital, Peking University, Beijing, China.
Mingxiao LiDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Ming LiDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Xiaohui RenDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Song LinDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. linsong2005@126.com.
Yong CuiDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. cuiyong20130422@163.com.

Funding

National Natural Science Foundation of China No.82202983Peking University Clinical Scientist Training Program, supported by "the Fundamental Research Funds for the Central Universities" BMU2024PYJH002
6 · The paper itself

Abstract

purposeThe 2021 WHO classification of central nervous system tumors introduced molecular criteria to stratify Grade 4 gliomas, which remain heterogeneous. This study aims to elucidate the clinical, radiological, and molecular characteristics of WHO Grade 4 gliomas, focusing on their prognostic implications and the development of a predictive model for astrocytoma IDH-mutant WHO Grade 4 (A4).

methodsA retrospective cohort of 223 patients from Beijing Tiantan Hospital was analyzed. Clinical, radiological, and histopathological data were combined with molecular profiling, focusing on IDH mutations, TERT promoter mutations, and MGMT methylation. A predictive model was developed using LASSO regression to distinguish A4 from glioblastomas and validated with an external dataset from UCSF.

resultsThe cohort included 201 glioblastomas (90.1%) and 22 A4 cases (9.9%). A4 tumors were associated with younger age, higher MGMT promoter methylation, lower rates of TERT mutations, and distinct radiological features, such as cortical non-enhancing tumor infiltration (CnCE). Patients with A4 demonstrated significantly better survival outcomes compared to glioblastoma patients (p < 0.001). The predictive model for A4, incorporating age, tumor margin, and CnCE, achieved an AUC of 0.890 in the training set and 0.951 in the validation set.

conclusionIntegrating molecular and clinical criteria improves prognostication in Grade 4 gliomas. The predictive model developed in this study effectively identifies A4 tumors, facilitating more personalized therapeutic strategies.

Indexed as

GlioblastomaMolecular pathologyPredictive markerPrognosisWHO CNS 5

Identifiers

PMID39899184
PMCPMC11790548

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