Glioma research has evolved rapidly over the past decade, driven by breakthroughs in molecular biology, imaging technologies, and computational tools. Today, clinicians can classify tumors with far greater precision than ever before, using genetic mutations, epigenetic markers, and advanced diagnostic frameworks. Yet, despite this progress, an important question remains: are we truly capturing the full picture of what shapes patient outcomes?

Traditionally, glioma classification has focused on what can be measured in the tumor itself—its histology, molecular profile, and biological behavior. While these factors are undeniably critical, they may not fully explain why patients with similar tumors can experience very different clinical trajectories. Increasingly, researchers are beginning to recognize that broader influences—particularly social and environmental factors—may also play a role. Understanding how these different layers of information connect is becoming an important challenge in neuro-oncology.

A review was published in Volume 17 of Oncotarget on March 31, 2026, titled “Bibliometric mapping of glioma classification research through main path, key route, and K-core analyses.” The study was led by first and corresponding author Kayode Ahmed from The University of Texas MD Anderson Cancer Center, in collaboration with Juan E. Núñez-Ríos from Universidad Panamericana.

Full blog - https://www.oncotarget.org/2026/05/05/mapping-the-hidden-structure-of-glioma-research-what-are-we-missing/

Paper DOI - https://doi.org/10.18632/oncotarget.28851

Correspondence to - Kayode Ahmed - [email protected]

Abstract video - https://www.youtube.com/watch?v=v8h2z3eEMFM

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Keywords - cancer, glioma research, social network analysis, socio-clinical domains, web of science, networks

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