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Glioblastoma: a comprehensive approach combining bibliometric analysis, Latent Dirichlet Allocation, and HJ-Biplot

Glioblastoma insights and trends: a 49-year bibliometric analysis

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Abstract

Glioblastoma is a common and aggressive malignant central nervous system tumor in adults. This study aims to evaluate and analyze the scientific results, collaboration countries, main research topics, and topics over time reported about glioblastoma. A bibliometric analysis of glioblastoma publications was performed mainly using R and Multbiplot software for author, journal, and resume. Associated statistic methods Latent Dirichlet Allocation (LDA) and HJ-Biplot. Inclusion criteria were research articles from the PubMed database published in English between 1973 and December 2022. A total of 64,823 documents with an annual growth rate of 8.27% indicates a consistent increase in research output over time. The results for the number of citations and significant publications showed Cancer Res, J Neuro-Oncol, and Neuro-Oncology are the most influential journals in the field of glioblastoma. The countries that concentrated research were the tumor United States, China, Germany, and Italy. Finally, there has been a marked growth in studies on prognosis and patient survival, therapies, and treatments for glioblastoma. These findings reinforce the need for increased global resources to address glioblastoma, particularly in underdeveloped countries. Glioblastoma research’s exponential growth reflects sustained interest in early diagnosis and patient survival.

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No datasets were generated or analysed during the current study.

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Acknowledgements

This study was supported by the Department of Mathematics and Statistics, Institute of Basic Sciences and Research Institute of the Technical University of Manabí.

Funding

The authors received no specific funding for this work.

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Authors and Affiliations

  1. Departamento de Matemáticas y Estadística, Faculta de Ciencias Básicas, Universidad Técnica de Manabí, Portoviejo, 130105, Ecuador

    Karime Montes-Escobar & Rosalba Karen Bravo-Saltos

  2. Statistics Department, University of Salamanca, Salamanca, 37007, Spain

    Karime Montes-Escobar & Javier de la Hoz-M

  3. Universidad del Magdalena, Santa Marta, 470004, Colombia

    Javier de la Hoz-M

  4. SOLCA Nucleus Loja, Loja, 110105, Ecuador

    Paul Castillo-Cordova

  5. Carrera de Medicina, Facultad de Ciencias de la Salud,, Universidad Técnica Particular de Loja, Loja, 1101608 , Ecuador

    Paul Castillo-Cordova

  6. Optometry Carrer, Faculty of Health Sciences, Universidad Técnica de Manabí, Portoviejo, 130105, Ecuador

    Julia Patricia Duran-Ospina

  7. School of Medicine, Faculty of Health Sciences, Universidad Técnica de Manabí, Portoviejo, 130105, Ecuador

    German Josuet Lapo-Talledo & Aline Siteneski

  8. Research Institute, Faculty of Health Sciences, Medicine Career, Universidad Técnica de Manabí, Portoviejo, 130105, Ecuador

    Aline Siteneski

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  1. Karime Montes-Escobar
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Contributions

Conceptualization, JHM. and KME.; methodology, JHM., KME.; software, JHM., KME.; validation, JHM., and KME.; formal analysis, JHM and KME resources, JPDO.; data curation, JHM., KME, AS; writing original draft preparation, AS, GJLT;writing review and editing, AS, JPDO.; visualization and supervision, RKBS and PCC.; funding acquisition, PCC, JPDO and RKBS.

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Correspondence to Aline Siteneski.

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Montes-Escobar, K., de la Hoz-M, J., Castillo-Cordova, P. et al. Glioblastoma: a comprehensive approach combining bibliometric analysis, Latent Dirichlet Allocation, and HJ-Biplot. Neurosurg Rev 47, 209 (2024). https://doi.org/10.1007/s10143-024-02440-x

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