A Machine-Learning-Based Bibliometric Analysis of the Scientific Literature on Anal Cancer

Pierfrancesco Franco, Eva Segelov, Anders Johnsson, Rachel Riechelmann, Marianne G. Guren, Prajnan Das, Sheela Rao, Dirk Arnold, Karen Lise Garm Spindler, Eric Deutsch, Marco Krengli, Vincenzo Tombolini, David Sebag-Montefiore, Francesca De Felice

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)


Squamous-cell carcinoma of the anus (ASCC) is a rare disease. Barriers have been encountered to conduct clinical and translational research in this setting. Despite this, ASCC has been a prime example of collaboration amongst researchers. We performed a bibliometric analysis of ASCC-related literature of the last 20 years, exploring common patterns in research, tracking collaboration and identifying gaps. The electronic Scopus database was searched using the keywords “anal cancer”, to include manuscripts published in English, between 2000 and 2020. Data analysis was performed using R-Studio 0.98.1091 software. A machine-learning bibliometric method was applied. The bibliometrix R package was used. A total of 2322 scientific documents was found. The average annual growth rate in publication was around 40% during 2000–2020. The five most productive countries were United States of America (USA), United Kingdom (UK), France, Italy and Australia. The USA and UK had the greatest link strength of international collaboration (22.6% and 19.0%). Two main clusters of keywords for published research were identified: (a) prevention and screening and (b) overall management. Emerging topics included imaging, biomarkers and patient-reported outcomes. Further efforts are required to increase collaboration and funding to sustain future research in the setting of ASCC.

Original languageEnglish
Article number1697
Number of pages12
Issue number7
Publication statusPublished - 1 Apr 2022


  • anal cancer
  • bibliometrics
  • HIV
  • HPV
  • machine learning
  • oncology
  • radiotherapy
  • squamous-cell carcinoma

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