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Treatment in locally advanced rectal cancer: a machine learning bibliometric analysis

A bibliometric analysis was performed using a machine learning bibliometric methodology in order to evaluate the research trends in locally advanced rectal cancer treatment between 2000 and 2020. Information regarding publication outputs, countries, institutions, journals, keywords, funding, and cit...

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Autores principales: De Felice, Francesca, Crocetti, Daniele, Petrucciani, Niccolò, Belgioia, Liliana, Sapienza, Paolo, Bulzonetti, Nadia, Marampon, Francesco, Musio, Daniela, Tombolini, Vincenzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521411/
https://www.ncbi.nlm.nih.gov/pubmed/34671421
http://dx.doi.org/10.1177/17562848211042170
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author De Felice, Francesca
Crocetti, Daniele
Petrucciani, Niccolò
Belgioia, Liliana
Sapienza, Paolo
Bulzonetti, Nadia
Marampon, Francesco
Musio, Daniela
Tombolini, Vincenzo
author_facet De Felice, Francesca
Crocetti, Daniele
Petrucciani, Niccolò
Belgioia, Liliana
Sapienza, Paolo
Bulzonetti, Nadia
Marampon, Francesco
Musio, Daniela
Tombolini, Vincenzo
author_sort De Felice, Francesca
collection PubMed
description A bibliometric analysis was performed using a machine learning bibliometric methodology in order to evaluate the research trends in locally advanced rectal cancer treatment between 2000 and 2020. Information regarding publication outputs, countries, institutions, journals, keywords, funding, and citation counts was retrieved from Scopus database. During the search process, a total of 2370 publications were identified. The vast majority of papers originated from the United States of America, reflecting also its research drive in the collaboration network. Neoadjuvant treatment was the topic most studied in the highly cited studies. New keywords, including neoadjuvant chemotherapy, multiparametric magnetic resonance imaging, circulating tumor DNA, and genetic heterogeneity, appeared in the last 2 years. The quantity of publications on locally advanced rectal cancer treatment since 2000 showed an evolving research field. The ‘new’ keywords explain where research is presently heading.
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spelling pubmed-85214112021-10-19 Treatment in locally advanced rectal cancer: a machine learning bibliometric analysis De Felice, Francesca Crocetti, Daniele Petrucciani, Niccolò Belgioia, Liliana Sapienza, Paolo Bulzonetti, Nadia Marampon, Francesco Musio, Daniela Tombolini, Vincenzo Therap Adv Gastroenterol Review A bibliometric analysis was performed using a machine learning bibliometric methodology in order to evaluate the research trends in locally advanced rectal cancer treatment between 2000 and 2020. Information regarding publication outputs, countries, institutions, journals, keywords, funding, and citation counts was retrieved from Scopus database. During the search process, a total of 2370 publications were identified. The vast majority of papers originated from the United States of America, reflecting also its research drive in the collaboration network. Neoadjuvant treatment was the topic most studied in the highly cited studies. New keywords, including neoadjuvant chemotherapy, multiparametric magnetic resonance imaging, circulating tumor DNA, and genetic heterogeneity, appeared in the last 2 years. The quantity of publications on locally advanced rectal cancer treatment since 2000 showed an evolving research field. The ‘new’ keywords explain where research is presently heading. SAGE Publications 2021-10-16 /pmc/articles/PMC8521411/ /pubmed/34671421 http://dx.doi.org/10.1177/17562848211042170 Text en © The Author(s), 2021 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Review
De Felice, Francesca
Crocetti, Daniele
Petrucciani, Niccolò
Belgioia, Liliana
Sapienza, Paolo
Bulzonetti, Nadia
Marampon, Francesco
Musio, Daniela
Tombolini, Vincenzo
Treatment in locally advanced rectal cancer: a machine learning bibliometric analysis
title Treatment in locally advanced rectal cancer: a machine learning bibliometric analysis
title_full Treatment in locally advanced rectal cancer: a machine learning bibliometric analysis
title_fullStr Treatment in locally advanced rectal cancer: a machine learning bibliometric analysis
title_full_unstemmed Treatment in locally advanced rectal cancer: a machine learning bibliometric analysis
title_short Treatment in locally advanced rectal cancer: a machine learning bibliometric analysis
title_sort treatment in locally advanced rectal cancer: a machine learning bibliometric analysis
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521411/
https://www.ncbi.nlm.nih.gov/pubmed/34671421
http://dx.doi.org/10.1177/17562848211042170
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