Bibliometric mapping of glioma classification research through main path, key route, and K-core analyses

Kayode Ahmed1 and Juan E. Núñez-Ríos2 1 The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA 2 Universidad Panamericana, Facultad de Ciencias Económicas y Empresariales, Zapopan, Jalisco 45010, México Correspondence to: Kayode Ahmed, email:kmahmed@mdanderson.org Keywords: glioma research; social network analysis; socio-clinical domains; web of science; networks Received: September 22, 2025     Accepted: February 27, 2026     Published: March 31, 2026 Copyright: © 2026 Ahmed and Núñez-Ríos. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Burgeoning technological and clinical advances have significantly reshaped glioma classification. To assess the evolution of these changes, we analyzed bibliometric data from Web of Science to explore patterns in the socio-clinical domains of glioma classification research. Using network analysis, we built a direct citation network linking articles to authors, focusing on citations. Main Path Analysis provided an overview of research evolution, Key Route Analysis identified influential papers, and K-core analysis revealed densely connected articles. The network comprised 46,204 nodes and 231,432 arcs, highlighting DNA methylation profiling’s role in advancing molecular biomarker-based classification models. KRA emphasized advanced imaging and molecular techniques as key drivers, while K-core analysis identified articles cited at least 19 times. 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. Unlike previous studies that focused primarily on metrics such as the h-index, our approach identifies the limited but notable mention of social factors in glioma classification research, thereby highlighting a thematic gap. Through quantitative network analysis complemented by narrative interpretation, we uncovered patterns and substructures that offer deep insights into the evolving research landscape.