Predicting response to neoadjuvant therapy using image capture from diagnostic biopsies of oesophageal adenocarcinoma

Rahman, Saqib; Early, J.H.; Vries, Matt De; Lloyd, Matthew G.; Grace, Ben; Ramchurn, Gopal; Underwood, Timothy J.

doi:10.1016/j.ejso.2020.11.022

Cited by 1 publication

(4 citation statements)

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“…[43][44][45] RNA and whole genome sequencing (WGS) offer detailed and individualised data for analysis at the cost of expensive tissue analytical processes. 41 Computer vision-based ML promises comparatively low-cost, automated large-scale analysis in OC, although to date very few studies have applied such techniques to OC (Table 3). 41,46 Pilot work using convolutional neural networks (CNN) to process unlabelled high-resolution digital OAC histology slides achieved good internal validation in predicting response to NAT (C-index 0.836).…”

Section: Histopathological Analysismentioning

confidence: 99%

“…Great promise has been shown even in OC, in predicting outcomes following oesophagectomy. 41 However, while post-operative models have shown good discrimination and calibration, pre-operative models are more challenging. 12 Despite this, the pre-treatment MDT discussion remains a key mile marker in the patient’s care pathway, and optimising the decision-making at this stage is vital.…”

Section: A Role For Machine Learning?mentioning

confidence: 99%

“… 41 Computer vision–based ML promises comparatively low-cost, automated large-scale analysis in OC, although to date very few studies have applied such techniques to OC (Table 3 ). 41 , 46 Pilot work using convolutional neural networks (CNN) to process unlabelled high-resolution digital OAC histology slides achieved good internal validation in predicting response to NAT (C-index 0.836). 41 While these results are promising, validation over larger datasets and external data sources remains necessary before use in clinical practice, especially as the use of unsupervised learning creates a “black box” solution impeding transparency, “explainability”, and ultimately trust in the solution.…”

Section: Applications Within Oc To Datementioning

confidence: 99%

“… 41 , 46 Pilot work using convolutional neural networks (CNN) to process unlabelled high-resolution digital OAC histology slides achieved good internal validation in predicting response to NAT (C-index 0.836). 41 While these results are promising, validation over larger datasets and external data sources remains necessary before use in clinical practice, especially as the use of unsupervised learning creates a “black box” solution impeding transparency, “explainability”, and ultimately trust in the solution. An additional confounder in the Rahman et al study was the use of both NACRT and NACT within the patient cohort.…”

Section: Applications Within Oc To Datementioning

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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