Unraveling the heterogeneity of WHO grade 4 gliomas: insights from clinical, imaging, and molecular characterization
Abstract Purpose The 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 progno...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-02-01
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| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-025-01811-0 |
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| Summary: | Abstract Purpose The 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). Methods A 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. Results The 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. Conclusion Integrating 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. |
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| ISSN: | 2730-6011 |