"The findings indicate that the subset of articles focusing on glioma classification that incorporate social factors is relatively scarce in the analyzed data, in contrast to the prominence of epigenetic and imaging factors in the literature."
BUFFALO, NY – April 6, 2026 – A new 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 ."
Led by first and corresponding author Kayode Ahmed from The University of Texas MD Anderson Cancer Center, and Juan E. Núñez-Ríos from Universidad Panamericana , the study uses bibliometric network analysis to map how glioma classification research has evolved across clinical, molecular, and social domains. The authors analyzed Web of Science data using direct citation networks and applied main path analysis, key route analysis, and K-core analysis to identify influential papers, critical routes, and densely connected thematic clusters.
The network comprised 46,204 nodes and 231,432 arcs, highlighting the prominent role of DNA methylation profiling in molecular biomarker-based classification models. The authors also found that advanced imaging and molecular techniques were key drivers of the field, while the subset of glioma classification studies that incorporate social factors remained relatively scarce. Their analysis, therefore, points not only to the major intellectual structure of the literature but also to a thematic gap in how social determinants are represented in glioma classification research.
"Through quantitative network analysis complemented by narrative interpretation, we uncovered patterns and substructures that offer deep insights into the evolving research landscape."
The authors conclude that their framework offers a more integrative view of glioma classification research than approaches centered only on citation counts or h-index–style metrics. By identifying the evolutionary logic of the field and the limited but notable presence of social factors, the study suggests future glioma classification models may benefit from incorporating clinical, molecular, and social dimensions more explicitly.
DOI: https://doi.org/10.18632/oncotarget.28851
