Spatial Mapping of Gliomas: Validation of Radiographic Patterns under 2021 WHO Classification
Poster No:
1139
Submission Type:
Abstract Submission
Authors:
Yonghao Li1, Yizhou Wan1, Stephen Price1
Institutions:
1Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire
First Author:
Co-Author(s):
Stephen Price
Department of Clinical Neurosciences, University of Cambridge
Cambridge, Cambridgeshire
Department of Clinical Neurosciences, University of Cambridge
Cambridge, Cambridgeshire
Introduction:
The spatial distribution of gliomas is a key factor in prognosis and clinical outcomes. Gliomas, a heterogeneous group of brain tumors, vary in molecular profiles and spatial patterns, shaped by distinct cellular lineages and tumorigenic mechanisms. The 2021 WHO Classification integrates genetic markers like IDH mutation, 1p/19q co-deletion, and CDKN2A/B deletion to refine glioma subtypes, highlighting the importance of correlating biomarkers with tumor location for insights into pathogenesis and therapy [1]. Recent studies suggest glioma location mirrors brain embryologic development and precursor cell distribution [2]. For instance, IDH-mutant gliomas predominantly occur in the frontal lobe, while IDH wild-type glioblastomas show more variable localization [3]. However, most analyses lack validation in independent cohorts, limiting their generalizability. We analyzed the spatial distribution of biomarker-defined glioma subtypes under the 2021 WHO framework using preoperative MRI data and validated the findings in an independent cohort
Methods:
We analyzed preoperative anatomical MRI scans of 242 glioma patients to determine subtype-specific spatial distributions. Imaging data were registered to MNI-152 brain space and segmented into 84 standardized regions. Statistical correlations were drawn between tumor location, grade, overall survival and genetic biomarkers, including IDH mutation, MGMT methylation, 1p/19q co-deletion, and CDKN2A/B deletion. A validation cohort (n = 50) was added to confirm spatial trends observed in the initial dataset, employing identical segmentation, mapping, and statistical techniques.
Results:
The study generated 22 spatial frequency atlases for glioma subtypes, stratified by grade, subtype, and biomarker status, revealing distinct spatial distributions across 84 brain regions. IDH-mutant gliomas predominantly localized to the frontal lobe, while IDH wild-type glioblastomas showed a broader distribution, with higher frequencies in the temporal and parietal regions. The left temporal pole cortex had the highest tumor frequency, while the left lateral occipital cortex had the lowest. 1p/19q co-deleted oligodendrogliomas clustered in the frontal regions, particularly the frontal pole and superior frontal gyrus, whereas non-co-deleted tumors exhibited a more diffuse pattern. CDKN2A/B homozygous deletions, often seen in high-grade IDH-mutant astrocytomas, were preferentially located in the temporal lobe and insula. For MGMT methylation, unmethylated tumors predominantly occurred in the temporal lobe, correlating with poorer survival, while methylated tumors showed less specific localization and better prognosis. Survival analysis highlighted the temporal lobe-particularly the temporal pole and insula-as the region with the poorest survival rates, enriched for IDH wild-type and MGMT-unmethylated glioblastomas. By contrast, frontal lobe tumors, especially in the superior frontal gyrus and precentral gyrus, were associated with better survival, particularly in IDH-mutant gliomas. Validation in an independent cohort of 50 patients confirmed GBM's spatial trends, demonstrating the findings' reproducibility and clinical relevance.
Conclusions:
We developed 22 tumor frequency atlases correlating glioma spatial distribution with biomarker-defined subtypes and grades under the 2021 WHO classification. Our findings reveal that tumor location is not random but follows subtype-specific spatial patterns, with significant implications for prognosis and survival. The study underscores the value of integrating spatial and genetic analyses to better understand glioma heterogeneity. The identification of high-risk brain regions associated with poor survival outcomes offers valuable insights for personalized treatment planning and improved patient management. Future work will analyse regional transcriptomic variations explore correlation between transcriptomic and tumor location.
Modeling and Analysis Methods:
Classification and Predictive Modeling 1
Image Registration and Computational Anatomy
Segmentation and Parcellation
Other Methods
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Keywords:
Atlasing
Data analysis
MRI
1|2Indicates the priority used for review
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[2] Alcantara Llaguno, S. R., & Parada, L. F. (2011). Tumorigenesis: The origins of glioma. Nature Reviews Cancer, 11(11), 750–751.
[3] Kurosawa, R., Muragaki, Y., Iseki, H., & Okada, Y. (2014). Association between molecular alterations and tumor location and MRI features in anaplastic gliomas. Brain Tumor Pathology, 31(4), 229–236.