School of Cancer Sciences, Faculty of Medicine, University of Southampton, University Hospitals Southampton, Southampton, UK.
School of Electronics and Computer Science, University of Southampton, Southampton, UK.
J Gastrointest Surg. 2023 Apr;27(4):807-822. doi: 10.1007/s11605-022-05575-8. Epub 2023 Jan 23.
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.
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.
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.
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.
上消化道（UGI）多学科团队（MDT）的复杂性不断增加，导致临床医生的工作量、时间压力和需求增加。这增加了食管癌（OC）患者决策的异质性或“噪声”，并可能导致治疗决策不一致。近几十年来，人工智能（AI）的应用，特别是机器学习（ML）的分支，导致人们对统计建模在医疗保健中的应用效用产生了范式转变。在食管癌（OC）护理中，ML 技术已经成功应用于组织学样本和放射学成像的分析；然而，它尚未应用于 MDT 本身，在 MDT 中，此类模型可能受益于纳入信息丰富、多样化的数据集以提高预测模型准确性。
本综述讨论了 MDT 在现代 UGI 癌症治疗中的当前作用，以及迄今为止使用 ML 技术利用组织学和放射学数据预测治疗反应、预后、淋巴结疾病评估，甚至 OC 可切除性的情况。
综述发现，越来越多的证据支持 ML 工具在 OC 中多个与决策相关的领域中的应用，包括自动组织学分析和放射组学。然而，迄今为止，还没有专门针对 MDT 本身的特定应用，MDT 通常会整合这些信息。
作者认为 UGI MDT 提供了丰富多样的信息，其中 ML 有潜力实现标准化、自动化，并产生更一致、数据驱动的 MDT 决策。
