Clinically decisive (dis)agreement in multidisciplinary team assessment of esophageal squamous cell carcinoma; a prospective, national, multicenter study

Achiam, Michael Patrick; Nordsmark, Marianne; Ladekarl, Morten; Olsen, August A; Loft, Annika; Garbyal, Rajendra Singh; Larsen, M H; Ainsworth, Alan Patrick; Kristensen, Thomas Bjerring; Dikinis, Sarunas; Kjær, Daniel Willy; Bæksgaard, Lene; Siemsen, Mette; Nielsen, M. B.; Schlander, Sven; Kramer, Stine; Katballe, Niels; Kruhlikava, I; Tabaksblat, Elizaveta Mitkina; Fisker, Rune Vincents; Mortensen, P.B.; Holtved, Eva; Eckardt, Jens; Detlefsen, Sönke; Naujokaite, Gintare; Lütken, Christian D.

doi:10.1080/0284186x.2021.1937308

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…A small observational multicentre Danish study investigated inter-observer variability between MDT decisions at four major UGI cancer units in Denmark. 22 The study presented 20 OSCC cases as new referrals to each of the four centres to determine resectability, curability, and treatment strategy. The authors reviewed the frequency by which disagreement between MDTs resulted in a different treatment recommendation and whether this had a clinical impact.…”

Section: The Multidisciplinary Team (Mdt)mentioning

confidence: 99%

“…21 Individual experience, perception, and bias can also lead to discordance within that decision-making consistency, effectively a form of "noise" in the process. 22 Data-driven clinical decision tools are increasingly commonplace within medicine. The National Emergency Laparotomy Audit (NELA), for instance, has achieved widespread use for more objective operative risk stratification and the need for higher levels of care following emergency laparotomy.…”

Section: Introductionmentioning

confidence: 99%

“… 21 Individual experience, perception, and bias can also lead to discordance within that decision-making consistency, effectively a form of “noise” in the process. 22 …”

Section: Introductionmentioning

confidence: 99%

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The Oesophageal Cancer Multidisciplinary Team: Can Machine Learning Assist Decision-Making?

Thavanesan

Vigneswaran

Bodala

et al. 2023

Journal of Gastrointestinal Surgery

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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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Section: The Multidisciplinary Team (Mdt)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%