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Machine learning applications in upper gastrointestinal cancer surgery: a systematic review

BACKGROUND: Machine learning (ML) has seen an increase in application, and is an important element of a digital evolution. The role of ML within upper gastrointestinal surgery for malignancies has not been evaluated properly in the literature. Therefore, this systematic review aims to provide a comp...

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Autores principales: Bektaş, Mustafa, Burchell, George L., Bonjer, H. Jaap, van der Peet, Donald L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9839827/
https://www.ncbi.nlm.nih.gov/pubmed/35953684
http://dx.doi.org/10.1007/s00464-022-09516-z
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author Bektaş, Mustafa
Burchell, George L.
Bonjer, H. Jaap
van der Peet, Donald L.
author_facet Bektaş, Mustafa
Burchell, George L.
Bonjer, H. Jaap
van der Peet, Donald L.
author_sort Bektaş, Mustafa
collection PubMed
description BACKGROUND: Machine learning (ML) has seen an increase in application, and is an important element of a digital evolution. The role of ML within upper gastrointestinal surgery for malignancies has not been evaluated properly in the literature. Therefore, this systematic review aims to provide a comprehensive overview of ML applications within upper gastrointestinal surgery for malignancies. METHODS: A systematic search was performed in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only included when they described machine learning in upper gastrointestinal surgery for malignancies. The Cochrane risk-of-bias tool was used to determine the methodological quality of studies. The accuracy and area under the curve were evaluated, representing the predictive performances of ML models. RESULTS: From a total of 1821 articles, 27 studies met the inclusion criteria. Most studies received a moderate risk-of-bias score. The majority of these studies focused on neural networks (n = 9), multiple machine learning (n = 8), and random forests (n = 3). Remaining studies involved radiomics (n = 3), support vector machines (n = 3), and decision trees (n = 1). Purposes of ML included predominantly prediction of metastasis, detection of risk factors, prediction of survival, and prediction of postoperative complications. Other purposes were predictions of TNM staging, chemotherapy response, tumor resectability, and optimal therapy. CONCLUSIONS: Machine Learning algorithms seem to contribute to the prediction of postoperative complications and the course of disease after upper gastrointestinal surgery for malignancies. However, due to the retrospective character of ML studies, these results require trials or prospective studies to validate this application of ML. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00464-022-09516-z.
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spelling pubmed-98398272023-01-15 Machine learning applications in upper gastrointestinal cancer surgery: a systematic review Bektaş, Mustafa Burchell, George L. Bonjer, H. Jaap van der Peet, Donald L. Surg Endosc Review Article BACKGROUND: Machine learning (ML) has seen an increase in application, and is an important element of a digital evolution. The role of ML within upper gastrointestinal surgery for malignancies has not been evaluated properly in the literature. Therefore, this systematic review aims to provide a comprehensive overview of ML applications within upper gastrointestinal surgery for malignancies. METHODS: A systematic search was performed in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only included when they described machine learning in upper gastrointestinal surgery for malignancies. The Cochrane risk-of-bias tool was used to determine the methodological quality of studies. The accuracy and area under the curve were evaluated, representing the predictive performances of ML models. RESULTS: From a total of 1821 articles, 27 studies met the inclusion criteria. Most studies received a moderate risk-of-bias score. The majority of these studies focused on neural networks (n = 9), multiple machine learning (n = 8), and random forests (n = 3). Remaining studies involved radiomics (n = 3), support vector machines (n = 3), and decision trees (n = 1). Purposes of ML included predominantly prediction of metastasis, detection of risk factors, prediction of survival, and prediction of postoperative complications. Other purposes were predictions of TNM staging, chemotherapy response, tumor resectability, and optimal therapy. CONCLUSIONS: Machine Learning algorithms seem to contribute to the prediction of postoperative complications and the course of disease after upper gastrointestinal surgery for malignancies. However, due to the retrospective character of ML studies, these results require trials or prospective studies to validate this application of ML. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00464-022-09516-z. Springer US 2022-08-11 2023 /pmc/articles/PMC9839827/ /pubmed/35953684 http://dx.doi.org/10.1007/s00464-022-09516-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Review Article
Bektaş, Mustafa
Burchell, George L.
Bonjer, H. Jaap
van der Peet, Donald L.
Machine learning applications in upper gastrointestinal cancer surgery: a systematic review
title Machine learning applications in upper gastrointestinal cancer surgery: a systematic review
title_full Machine learning applications in upper gastrointestinal cancer surgery: a systematic review
title_fullStr Machine learning applications in upper gastrointestinal cancer surgery: a systematic review
title_full_unstemmed Machine learning applications in upper gastrointestinal cancer surgery: a systematic review
title_short Machine learning applications in upper gastrointestinal cancer surgery: a systematic review
title_sort machine learning applications in upper gastrointestinal cancer surgery: a systematic review
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9839827/
https://www.ncbi.nlm.nih.gov/pubmed/35953684
http://dx.doi.org/10.1007/s00464-022-09516-z
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