Cargando…
A Machine-Learning-Based Bibliometric Analysis of the Scientific Literature on Anal Cancer
SIMPLE SUMMARY: Squamous-cell carcinoma of the anus, being a rare cancer, requires national and international collaborations, networking, organizational proficiency and leadership to overcome barriers towards the implementation of clinical trials to establish improved standards of care treatment str...
Autores principales: | , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8996998/ https://www.ncbi.nlm.nih.gov/pubmed/35406469 http://dx.doi.org/10.3390/cancers14071697 |
_version_ | 1784684604315140096 |
---|---|
author | Franco, Pierfrancesco Segelov, Eva Johnsson, Anders Riechelmann, Rachel Guren, Marianne G. Das, Prajnan Rao, Sheela Arnold, Dirk Spindler, Karen-Lise Garm Deutsch, Eric Krengli, Marco Tombolini, Vincenzo Sebag-Montefiore, David De Felice, Francesca |
author_facet | Franco, Pierfrancesco Segelov, Eva Johnsson, Anders Riechelmann, Rachel Guren, Marianne G. Das, Prajnan Rao, Sheela Arnold, Dirk Spindler, Karen-Lise Garm Deutsch, Eric Krengli, Marco Tombolini, Vincenzo Sebag-Montefiore, David De Felice, Francesca |
author_sort | Franco, Pierfrancesco |
collection | PubMed |
description | SIMPLE SUMMARY: Squamous-cell carcinoma of the anus, being a rare cancer, requires national and international collaborations, networking, organizational proficiency and leadership to overcome barriers towards the implementation of clinical trials to establish improved standards of care treatment strategies and the conduction of translational research projects to shed light into its biology and molecular characterization. The purpose of the present study is to obtain a global frame of the scientific literature related to anal cancer, through a bibliometric analysis of the published articles during the last 20 years (2000–2020), exploring trends and common patterns in research, tracking collaboration and networks to foresee future directions in basic and clinical research. ABSTRACT: 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. |
format | Online Article Text |
id | pubmed-8996998 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89969982022-04-12 A Machine-Learning-Based Bibliometric Analysis of the Scientific Literature on Anal Cancer Franco, Pierfrancesco Segelov, Eva Johnsson, Anders Riechelmann, Rachel Guren, Marianne G. Das, Prajnan Rao, Sheela Arnold, Dirk Spindler, Karen-Lise Garm Deutsch, Eric Krengli, Marco Tombolini, Vincenzo Sebag-Montefiore, David De Felice, Francesca Cancers (Basel) Article SIMPLE SUMMARY: Squamous-cell carcinoma of the anus, being a rare cancer, requires national and international collaborations, networking, organizational proficiency and leadership to overcome barriers towards the implementation of clinical trials to establish improved standards of care treatment strategies and the conduction of translational research projects to shed light into its biology and molecular characterization. The purpose of the present study is to obtain a global frame of the scientific literature related to anal cancer, through a bibliometric analysis of the published articles during the last 20 years (2000–2020), exploring trends and common patterns in research, tracking collaboration and networks to foresee future directions in basic and clinical research. ABSTRACT: 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. MDPI 2022-03-27 /pmc/articles/PMC8996998/ /pubmed/35406469 http://dx.doi.org/10.3390/cancers14071697 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Franco, Pierfrancesco Segelov, Eva Johnsson, Anders Riechelmann, Rachel Guren, Marianne G. Das, Prajnan Rao, Sheela Arnold, Dirk Spindler, Karen-Lise Garm Deutsch, Eric Krengli, Marco Tombolini, Vincenzo Sebag-Montefiore, David De Felice, Francesca A Machine-Learning-Based Bibliometric Analysis of the Scientific Literature on Anal Cancer |
title | A Machine-Learning-Based Bibliometric Analysis of the Scientific Literature on Anal Cancer |
title_full | A Machine-Learning-Based Bibliometric Analysis of the Scientific Literature on Anal Cancer |
title_fullStr | A Machine-Learning-Based Bibliometric Analysis of the Scientific Literature on Anal Cancer |
title_full_unstemmed | A Machine-Learning-Based Bibliometric Analysis of the Scientific Literature on Anal Cancer |
title_short | A Machine-Learning-Based Bibliometric Analysis of the Scientific Literature on Anal Cancer |
title_sort | machine-learning-based bibliometric analysis of the scientific literature on anal cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8996998/ https://www.ncbi.nlm.nih.gov/pubmed/35406469 http://dx.doi.org/10.3390/cancers14071697 |
work_keys_str_mv | AT francopierfrancesco amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT segeloveva amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT johnssonanders amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT riechelmannrachel amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT gurenmarianneg amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT dasprajnan amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT raosheela amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT arnolddirk amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT spindlerkarenlisegarm amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT deutscheric amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT krenglimarco amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT tombolinivincenzo amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT sebagmontefioredavid amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT defelicefrancesca amachinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT francopierfrancesco machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT segeloveva machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT johnssonanders machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT riechelmannrachel machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT gurenmarianneg machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT dasprajnan machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT raosheela machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT arnolddirk machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT spindlerkarenlisegarm machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT deutscheric machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT krenglimarco machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT tombolinivincenzo machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT sebagmontefioredavid machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer AT defelicefrancesca machinelearningbasedbibliometricanalysisofthescientificliteratureonanalcancer |