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The Oesophageal cancer multidisciplinary team: can machine learning assist decision‑making?

The Oesophageal cancer multidisciplinary team: can machine learning assist decision‑making?
The Oesophageal cancer multidisciplinary team: can machine learning assist decision‑making?
Background
The complexity of the upper gastrointestinal (UGI) multidisciplinary team (MDT) is continually growing, leading to rising clinician workload, time pressures, and demands. This increases heterogeneity or ‘noise’ within decision-making for patients with oesophageal cancer (OC) and may lead to inconsistent treatment decisions. In recent decades, the application of artificial intelligence (AI) and more specifically the branch of machine learning (ML) has led to a paradigm shift in the perceived utility of statistical modelling within healthcare. Within oesophageal cancer (OC) care, ML techniques have already been applied with early success to the analyses of histological samples and radiology imaging; however, it has not yet been applied to the MDT itself where such models are likely to benefit from incorporating information-rich, diverse datasets to increase predictive model accuracy.

Methods
This review discusses the current role the MDT plays in modern UGI cancer care as well as the utilisation of ML techniques to date using histological and radiological data to predict treatment response, prognostication, nodal disease evaluation, and even resectability within OC.

Results
The review finds that an emerging body of evidence is growing in support of ML tools within multiple domains relevant to decision-making within OC including automated histological analysis and radiomics. However, to date, no specific application has been directed to the MDT itself which routinely assimilates this information.

Conclusions
The authors feel the UGI MDT offers an information-rich, diverse array of data from which ML offers the potential to standardise, automate, and produce more consistent, data-driven MDT decisions.
Artificial intelligence, Machine learning, Multidisciplinary team, Oesophageal cancer
1941-6636
807-822
Thavanesan, Navamayooran
94f0a216-9131-431b-809c-f5a83146343d
Vigneswaran, Ganesh
4e3865ad-1a15-4a27-b810-55348e7baceb
Bodala, Indu
aa030b32-7159-4bc7-beb4-50df4ec84944
Underwood, Timothy
8e81bf60-edd2-4b0e-8324-3068c95ea1c6
Thavanesan, Navamayooran
94f0a216-9131-431b-809c-f5a83146343d
Vigneswaran, Ganesh
4e3865ad-1a15-4a27-b810-55348e7baceb
Bodala, Indu
aa030b32-7159-4bc7-beb4-50df4ec84944
Underwood, Timothy
8e81bf60-edd2-4b0e-8324-3068c95ea1c6

Thavanesan, Navamayooran, Vigneswaran, Ganesh, Bodala, Indu and Underwood, Timothy (2023) The Oesophageal cancer multidisciplinary team: can machine learning assist decision‑making? Journal of Gastrointestinal Cancer, 27 (4), 807-822. (doi:10.1007/s11605-022-05575-8).

Record type: Article

Abstract

Background
The complexity of the upper gastrointestinal (UGI) multidisciplinary team (MDT) is continually growing, leading to rising clinician workload, time pressures, and demands. This increases heterogeneity or ‘noise’ within decision-making for patients with oesophageal cancer (OC) and may lead to inconsistent treatment decisions. In recent decades, the application of artificial intelligence (AI) and more specifically the branch of machine learning (ML) has led to a paradigm shift in the perceived utility of statistical modelling within healthcare. Within oesophageal cancer (OC) care, ML techniques have already been applied with early success to the analyses of histological samples and radiology imaging; however, it has not yet been applied to the MDT itself where such models are likely to benefit from incorporating information-rich, diverse datasets to increase predictive model accuracy.

Methods
This review discusses the current role the MDT plays in modern UGI cancer care as well as the utilisation of ML techniques to date using histological and radiological data to predict treatment response, prognostication, nodal disease evaluation, and even resectability within OC.

Results
The review finds that an emerging body of evidence is growing in support of ML tools within multiple domains relevant to decision-making within OC including automated histological analysis and radiomics. However, to date, no specific application has been directed to the MDT itself which routinely assimilates this information.

Conclusions
The authors feel the UGI MDT offers an information-rich, diverse array of data from which ML offers the potential to standardise, automate, and produce more consistent, data-driven MDT decisions.

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More information

Accepted/In Press date: 10 December 2022
e-pub ahead of print date: 23 January 2023
Published date: April 2023
Additional Information: Funding Information: The authors wish to acknowledge the Institute for Life Sciences and University Hospital Southampton who jointly provide a funded studentship for NT. Publisher Copyright: © 2023, The Author(s).
Keywords: Artificial intelligence, Machine learning, Multidisciplinary team, Oesophageal cancer

Identifiers

Local EPrints ID: 474656
URI: http://eprints.soton.ac.uk/id/eprint/474656
ISSN: 1941-6636
PURE UUID: 9a587222-2c20-45c8-a618-d08b0a3a4ffe
ORCID for Ganesh Vigneswaran: ORCID iD orcid.org/0000-0002-4115-428X
ORCID for Timothy Underwood: ORCID iD orcid.org/0000-0001-9455-2188

Catalogue record

Date deposited: 28 Feb 2023 17:57
Last modified: 17 Mar 2024 04:06

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Contributors

Author: Navamayooran Thavanesan
Author: Indu Bodala

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