New Insights into Glioma Intratumoral Networks
Poster No:
1384
Submission Type:
Abstract Submission
Authors:
Hongbo Bao1,2, Yinyan Wang1, Haris Sair3
Institutions:
1Beijing Tiantan Hospital, Capital Medical University, Beijing, China, 2Harbin Medical University Cancer Hospital, Harbin, China, 3Johns Hopkins University School of Medicine, Baltimore, MD
First Author:
Hongbo Bao
Beijing Tiantan Hospital, Capital Medical University|Harbin Medical University Cancer Hospital
Beijing, China|Harbin, China
Beijing Tiantan Hospital, Capital Medical University|Harbin Medical University Cancer Hospital
Beijing, China|Harbin, China
Co-Author(s):
Introduction:
Gliomas are not isolated pathologies but dynamic entities that interact with neuronal circuits (Jung et al., 2019; Krishna et al., 2023), disrupting normal brain networks while forming new functional connections (Mandal, Brem, & Suckling, 2023; Venkataramani, Tanev, Kuner, Wick, & Winkler, 2021). These interactions significantly influence clinical manifestations, including cognitive deficits and motor impairments. However, the extent to which glioma-induced functional network changes correlate with tumor pathology and patient outcomes remains underexplored. This study examines the relationships between intratumoral functional regions, pathological characteristics, brain network features, and clinical outcomes, providing insights into the neural mechanisms underlying glioma progression and prognosis.
Methods:
Resting-state fMRI data from 306 glioma patients across two centers were analyzed using an individualized parcellation method to delineate intratumoral functional networks. Tumors were segmented into core and peritumoral regions, with functional connectivity analyzed relative to Yeo 17 brain networks. Associations between the ratio of the intratumoral functional regions to tumor volume (RIFR), glioma pathology (WHO grade, IDH mutation, EGFR status), brain network features, and clinical outcomes (KPS, epilepsy, cognitive and language function, survival) were assessed. Prognostic models based on functional connectivity were developed using machine learning techniques.
Results:
Intratumoral functional regions were present in all glioma subtypes, with significant variability in their proportions (Figures 1A-D). Higher RIFR was associated with gliomas of lower grades and favorable molecular profiles, such as IDH mutation and EGFR negativity (Figures 2A-C). Patients with worse clinical symptoms, including lower KPS scores and epilepsy, exhibited larger absolute volumes of intratumoral functional regions (Figures 2D-E). RIFR correlated positively with ReHo, characteristic path length, clustering coefficient, and local efficiency, but negatively with global efficiency, indicating its relationship with whole-brain network disruptions (Figures 2F-J). Lower RIFR was linked to more severe cognitive and language impairments, suggesting its role in functional deficits (Figures 2K-L). Survival analysis revealed that patients with lower RIFR had significantly better outcomes (Figure 2M). Prognostic models incorporating RIFR and other network metrics demonstrated high predictive accuracy for two-year survival (AUC = 0.82 for Random Forest, AUC = 0.81 for SVM; Figure 2N), emphasizing the clinical relevance of glioma-related functional network features.
·Figure 1. Individual parcellation of functional networks in glioma.
·Figure 2. Integrative analysis of glioma intratumoral regions with pathological characteristics, brain network features, and clinical outcomes.
Conclusions:
This study highlights the importance of individualized functional mapping in glioma patients, showing that intratumoral functional regions are closely tied to tumor pathology, whole brain networks, and clinical outcomes. These findings suggest that integrating brain network alterations can enhance prognostic accuracy and guide personalized treatment strategies for glioma patients.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
FUNCTIONAL MRI
Other - Glioma; Brain networks; Clinical outcomes
1|2Indicates the priority used for review
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Provide references using APA citation style.
Krishna, S., Choudhury, A., Keough, M. B., Seo, K., Ni, L., Kakaizada, S., . . . Hervey-Jumper, S. L. (2023). Glioblastoma remodelling of human neural circuits decreases survival. Nature. doi:10.1038/s41586-023-06036-1
Mandal, A. S., Brem, S., & Suckling, J. (2023). Brain network mapping and glioma pathophysiology. Brain Commun, 5(2), fcad040. doi:10.1093/braincomms/fcad040
Venkataramani, V., Tanev, D. I., Kuner, T., Wick, W., & Winkler, F. (2021). Synaptic input to brain tumors: clinical implications. Neuro Oncol, 23(1), 23-33. doi:10.1093/neuonc/noaa158