Research Article
BibTex RIS Cite

Year 2025, Volume: 11 Issue: 3, 639 - 653, 04.05.2025

Turgut Kuytu

https://doi.org/10.18621/eurj.1630953

Abstract

References

  • 1. de Biase D, Franceschi E, Marucci G. Editorial: Advances in brain tumors diagnosis and treatment. Front Med (Lausanne). 2023;10:1152547. doi: 10.3389/fmed.2023.1152547.
  • 2. Samee NA, Mahmoud NF, Atteia G, et al. Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model. Diagnostics (Basel). 2022;12(10):2541. doi: 10.3390/diagnostics12102541.
  • 3. Youssef G, Wen PY. Medical and Neurological Management of Brain Tumor Complications. Curr Neurol Neurosci Rep. 2021;21(10):53. doi: 10.1007/s11910-021-01142-x.
  • 4. Ostrom QT, Cioffi G, Waite K, Kruchko C, Barnholtz-Sloan JS. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014-2018. Neuro Oncol. 2021;23(12 Suppl 2):iii1-iii105. doi: 10.1093/neuonc/noab200.
  • 5. Chen C. Science Mapping: A Systematic Review of the Literature. J Data Inf Sci. 2017;2(2):1-40. doi: 10.1515/jdis-20170006.
  • 6. Li K, Rollins J, Yan E. Web of Science use in published research and review papers 1997-2017: a selective, dynamic, cross-domain, content-based analysis. Scientometrics. 2018;115(1):1-20. doi: 10.1007/s11192-017-2622-5.
  • 7. Giorgi FM, Ceraolo C, Mercatelli D. The R Language: An Engine for Bioinformatics and Data Science. Life (Basel). 2022;12(5):648. doi: 10.3390/life12050648.
  • 8. Aria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr. 2017;11(4):959-975. doi: 10.1016/j.joi.2017.08.007.
  • 9. Shah FA, Jawaid SA. The h-Index: An Indicator of Research and Publication Output. Pak J Med Sci. 2023;39(2):315-316. doi: 10.12669/pjms.39.2.7398.
  • 10. Ali MJ. Understanding the 'g-index' and the 'e-index'. Semin Ophthalmol. 2021;36(4):139. doi: 10.1080/08820538.2021.1922975.
  • 11. Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021;23(8):1231-1251. doi: 10.1093/neuonc/noab106.
  • 12. Fei X, Zhao J, Wei W, et al. Clinical, Radiological, Pathological Features and Seizure Outcome With Surgical Management of Polymorphous Low-Grade Neuroepithelial Tumor of the Young Associated With Epilepsy. Front Oncol. 2022;12:863373. doi: 10.3389/fonc.2022.863373.
  • 13. Rehman A, Naz S, Razzak MI, Akram F, Imran M. A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning. Circuits, Syst Signal Process. 2020;39:757-775. doi: 10.1007/s00034-019-01246-3.
  • 14. Ullah N, Khan JA, Khan MS, et al. An Effective Approach to Detect and Identify Brain Tumors using Transfer Learning. Appl Sci. 2022;12(11):5645. doi: 10.3390/app12115645.
  • 15. Havaei M, Davy A, Warde-Farley D, e. Brain tumor segmentation with Deep Neural Networks. Med Image Anal. 2017;35:18-31. doi: 10.1016/j.media.2016.05.004.
  • 16. Singh SK, Clarke ID, Terasaki M, Bonn VE, Hawkins C, Squire J, Dirks PB. Identification of a cancer stem cell in human brain tumors. Cancer Res. 2003;63(18):5821-5828.
  • 17. Lee KJ. Chapter Seven - Architecture of neural processing unit for deep neural networks. In: Kim S, Deka GCBT-A in C, editors. Hardware Accelerator Systems for Artificial Intelligence and Machine Learning. Elsevier; 2021. p. 217-45.
  • 18. Schaff LR, Mellinghoff IK. Glioblastoma and Other Primary Brain Malignancies in Adults: A Review. JAMA. 2023;329(7):574-587. doi: 10.1001/jama.2023.0023.
  • 19. Agosti E, Zeppieri M, De Maria L, et al. Glioblastoma Immunotherapy: A Systematic Review of the Present Strategies and Prospects for Advancements. Int J Mol Sci. 2023;24(20):15037. doi: 10.3390/ijms242015037.
  • 20. Choi JY. Medulloblastoma: Current Perspectives and Recent Advances. Brain Tumor Res Treat. 2023;11(1):28-38. doi: 10.14791/btrt.2022.0046.
  • 21. Sarker IH. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Comput Sci. 2021;2(6):420. doi: 10.1007/s42979-021-00815-1.
  • 22. Murthy MYB, Koteswararao A, Babu MS. Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis. Biomed Eng Lett. 2021;12(1):37-58. doi: 10.1007/s13534-021-00209-5.
  • 23. Mehrotra R, Ansari MA, Agrawal R, Anand RS. A transfer learning approach for AI-based classification of brain tumors. Mach Learn Appl. 2020;2:100003. doi: 10.1016/j.mlwa.2020.100003.
  • 24. Bhatele KR, Bhadauria SS. Machine learning application in Glioma classification: review and comparison analysis. Arch Comput Methods Eng. 2022;29:247-2741-28. doi: 10.1007/s11831-021-09572-z.
  • 25. Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021;23(8):1231-1251. doi: 10.1093/neuonc/noab106.
  • 26. Torp SH, Solheim O, Skjulsvik AJ. The WHO 2021 Classification of Central Nervous System tumours: a practical update on what neurosurgeons need to know-a minireview. Acta Neurochir (Wien). 2022;164(9):2453-2464. doi: 10.1007/s00701-022-05301-y.
  • 27. Gue R, Lakhani DA. The 2021 World Health Organization Central Nervous System Tumor Classification: The Spectrum of Diffuse Gliomas. Biomedicines. 2024;12(6):1349. doi: 10.3390/biomedicines12061349.
  • 28. Arvanitis CD, Ferraro GB, Jain RK. The blood-brain barrier and blood-tumour barrier in brain tumours and metastases. Nat Rev Cancer. 2020;20(1):26-41. doi: 10.1038/s41568-019-0205-x.
  • 29. Ullah I, Chung K, Bae S, et al. Nose-to-Brain Delivery of Cancer-Targeting Paclitaxel-Loaded Nanoparticles Potentiates Antitumor Effects in Malignant Glioblastoma. Mol Pharm. 2020 Apr 6;17(4):1193-1204. doi: 10.1021/acs.molpharmaceut.9b01215.
  • 30. Upton DH, Ung C, George SM, Tsoli M, Kavallaris M, Ziegler DS. Challenges and opportunities to penetrate the blood-brain barrier for brain cancer therapy. Theranostics. 2022;12(10):4734-4752. doi: 10.7150/thno.69682.

Brain tumors. A bibliometric analysis of forty years by science mapping

Year 2025, Volume: 11 Issue: 3, 639 - 653, 04.05.2025

Turgut Kuytu

https://doi.org/10.18621/eurj.1630953

Abstract

Objective: Science mapping is a systematic approach to analyzing the intricate network of relationships in the scientific literature. Science mapping methodology investigates the networks of relationships among scientific articles, authors, journals, keywords, and research topics. This study aims to comprehend the literature in the field of brain tumors.

Methods: Our study covers the period 1980-2022. Our study uses the Web of Science database for literature reviews and bibliometric analyses. The obtained data were filtered and classified. The 10,777 articles were analyzed in five sections. Some of sections are: structural analysis of the articles, analysis of countries, keyword analysis, thematic analysis, and the collaboration analysis.

Results: The articles have been published in 1761 journals. The average citation per article is 38.22. The highest h and g-index values belong to Cancer Research. For thematic analysis, the period from 1980 to 2022 has been analyzed. During 2021-2022, 'Deep Learning' and 'Brain Tumors' formed the motor themes. The authors' collaboration network is analyzed. Kun LE is the author with the most collaborations.

Conclusions: Upon examining thematic maps from all periods, it is assessed that the likely topics and scopes of future research on brain tumors will be biomarkers, personalized treatments, artificial intelligence, immunotherapy, and pediatric brain tumors.

Keywords

Brain tumors science mapping thematic analysis literature web of science

Ethical Statement

This study was conducted entirely from open sources on the internet. It does not contain human or animal elements. Therefore, there is no need for the ethics committee's approval of the study.

References

  • 1. de Biase D, Franceschi E, Marucci G. Editorial: Advances in brain tumors diagnosis and treatment. Front Med (Lausanne). 2023;10:1152547. doi: 10.3389/fmed.2023.1152547.
  • 2. Samee NA, Mahmoud NF, Atteia G, et al. Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model. Diagnostics (Basel). 2022;12(10):2541. doi: 10.3390/diagnostics12102541.
  • 3. Youssef G, Wen PY. Medical and Neurological Management of Brain Tumor Complications. Curr Neurol Neurosci Rep. 2021;21(10):53. doi: 10.1007/s11910-021-01142-x.
  • 4. Ostrom QT, Cioffi G, Waite K, Kruchko C, Barnholtz-Sloan JS. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014-2018. Neuro Oncol. 2021;23(12 Suppl 2):iii1-iii105. doi: 10.1093/neuonc/noab200.
  • 5. Chen C. Science Mapping: A Systematic Review of the Literature. J Data Inf Sci. 2017;2(2):1-40. doi: 10.1515/jdis-20170006.
  • 6. Li K, Rollins J, Yan E. Web of Science use in published research and review papers 1997-2017: a selective, dynamic, cross-domain, content-based analysis. Scientometrics. 2018;115(1):1-20. doi: 10.1007/s11192-017-2622-5.
  • 7. Giorgi FM, Ceraolo C, Mercatelli D. The R Language: An Engine for Bioinformatics and Data Science. Life (Basel). 2022;12(5):648. doi: 10.3390/life12050648.
  • 8. Aria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr. 2017;11(4):959-975. doi: 10.1016/j.joi.2017.08.007.
  • 9. Shah FA, Jawaid SA. The h-Index: An Indicator of Research and Publication Output. Pak J Med Sci. 2023;39(2):315-316. doi: 10.12669/pjms.39.2.7398.
  • 10. Ali MJ. Understanding the 'g-index' and the 'e-index'. Semin Ophthalmol. 2021;36(4):139. doi: 10.1080/08820538.2021.1922975.
  • 11. Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021;23(8):1231-1251. doi: 10.1093/neuonc/noab106.
  • 12. Fei X, Zhao J, Wei W, et al. Clinical, Radiological, Pathological Features and Seizure Outcome With Surgical Management of Polymorphous Low-Grade Neuroepithelial Tumor of the Young Associated With Epilepsy. Front Oncol. 2022;12:863373. doi: 10.3389/fonc.2022.863373.
  • 13. Rehman A, Naz S, Razzak MI, Akram F, Imran M. A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning. Circuits, Syst Signal Process. 2020;39:757-775. doi: 10.1007/s00034-019-01246-3.
  • 14. Ullah N, Khan JA, Khan MS, et al. An Effective Approach to Detect and Identify Brain Tumors using Transfer Learning. Appl Sci. 2022;12(11):5645. doi: 10.3390/app12115645.
  • 15. Havaei M, Davy A, Warde-Farley D, e. Brain tumor segmentation with Deep Neural Networks. Med Image Anal. 2017;35:18-31. doi: 10.1016/j.media.2016.05.004.
  • 16. Singh SK, Clarke ID, Terasaki M, Bonn VE, Hawkins C, Squire J, Dirks PB. Identification of a cancer stem cell in human brain tumors. Cancer Res. 2003;63(18):5821-5828.
  • 17. Lee KJ. Chapter Seven - Architecture of neural processing unit for deep neural networks. In: Kim S, Deka GCBT-A in C, editors. Hardware Accelerator Systems for Artificial Intelligence and Machine Learning. Elsevier; 2021. p. 217-45.
  • 18. Schaff LR, Mellinghoff IK. Glioblastoma and Other Primary Brain Malignancies in Adults: A Review. JAMA. 2023;329(7):574-587. doi: 10.1001/jama.2023.0023.
  • 19. Agosti E, Zeppieri M, De Maria L, et al. Glioblastoma Immunotherapy: A Systematic Review of the Present Strategies and Prospects for Advancements. Int J Mol Sci. 2023;24(20):15037. doi: 10.3390/ijms242015037.
  • 20. Choi JY. Medulloblastoma: Current Perspectives and Recent Advances. Brain Tumor Res Treat. 2023;11(1):28-38. doi: 10.14791/btrt.2022.0046.
  • 21. Sarker IH. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Comput Sci. 2021;2(6):420. doi: 10.1007/s42979-021-00815-1.
  • 22. Murthy MYB, Koteswararao A, Babu MS. Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis. Biomed Eng Lett. 2021;12(1):37-58. doi: 10.1007/s13534-021-00209-5.
  • 23. Mehrotra R, Ansari MA, Agrawal R, Anand RS. A transfer learning approach for AI-based classification of brain tumors. Mach Learn Appl. 2020;2:100003. doi: 10.1016/j.mlwa.2020.100003.
  • 24. Bhatele KR, Bhadauria SS. Machine learning application in Glioma classification: review and comparison analysis. Arch Comput Methods Eng. 2022;29:247-2741-28. doi: 10.1007/s11831-021-09572-z.
  • 25. Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021;23(8):1231-1251. doi: 10.1093/neuonc/noab106.
  • 26. Torp SH, Solheim O, Skjulsvik AJ. The WHO 2021 Classification of Central Nervous System tumours: a practical update on what neurosurgeons need to know-a minireview. Acta Neurochir (Wien). 2022;164(9):2453-2464. doi: 10.1007/s00701-022-05301-y.
  • 27. Gue R, Lakhani DA. The 2021 World Health Organization Central Nervous System Tumor Classification: The Spectrum of Diffuse Gliomas. Biomedicines. 2024;12(6):1349. doi: 10.3390/biomedicines12061349.
  • 28. Arvanitis CD, Ferraro GB, Jain RK. The blood-brain barrier and blood-tumour barrier in brain tumours and metastases. Nat Rev Cancer. 2020;20(1):26-41. doi: 10.1038/s41568-019-0205-x.
  • 29. Ullah I, Chung K, Bae S, et al. Nose-to-Brain Delivery of Cancer-Targeting Paclitaxel-Loaded Nanoparticles Potentiates Antitumor Effects in Malignant Glioblastoma. Mol Pharm. 2020 Apr 6;17(4):1193-1204. doi: 10.1021/acs.molpharmaceut.9b01215.
  • 30. Upton DH, Ung C, George SM, Tsoli M, Kavallaris M, Ziegler DS. Challenges and opportunities to penetrate the blood-brain barrier for brain cancer therapy. Theranostics. 2022;12(10):4734-4752. doi: 10.7150/thno.69682.
There are 30 citations in total.

Details

Primary Language English
Subjects Brain and Nerve Surgery (Neurosurgery)
Journal Section Meta-Analysis
Authors

Turgut Kuytu 0000-0002-0505-3027

Early Pub Date April 16, 2025
Publication Date May 4, 2025
Submission Date January 31, 2025
Acceptance Date March 9, 2025
Published in Issue Year 2025 Volume: 11 Issue: 3

Cite

AMA Kuytu T. Brain tumors. A bibliometric analysis of forty years by science mapping. Eur Res J. May 2025;11(3):639-653. doi:10.18621/eurj.1630953
 Download Cover Image

e-ISSN: 2149-3189 


The European Research Journal, hosted by Turkish JournalPark ACADEMIC, is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

by-nc-nd.png

2025