Title: PathNAV – Early detection of pre-neoplastic changes in Barrett’s oesophagus using expert-knowledge augmented machine learning
Funder: Cancer Research UK

Abstract: Background The introduction of digital pathology techniques to systemically interrogate scanned tissue slides is set to revolutionise our approach to patient diagnosis and disease classification. Developing validated machine learning tools that can assist histopathologists, in particular in areas of diagnostic uncertainty, is a key translational research challenge. The bottleneck in developing these machine learning tools is the massive amount of expert-annotated imaging data needed to systematically train and validate clinical-grade image classifications systems. We now urgently need scalable computational resources that faithfully synthesise diagnostic decision-making processes to fully harness the potential of artificial intelligence in routine histopathology. Aims The purpose of this proposal is to develop and train a neural network for automated image analysis of a cancer precursor lesion by exploiting the wealth of user data generated from a large-scale digital pathology consensus study. Methods We will focus on Barrett’s oesophagus, the precursor lesion to oesophageal adenocarcinoma, as our model system. Members of the team have previously carried out the BOLERO Barrett’s dysplasia digital pathology consensus study (manuscript in preparation). This work involved over 50 pathologists from over 20 countries, which generated over 6,000 individual case diagnoses. This large-scale analysis revealed areas of diagnostic variation and defined criteria for guideline formulation of case review. We will exploit this powerful resource to carry out a digital pathology consensus study amongst 300 gastrointestinal pathologists in the UK. We will then leverage this extensive dataset in two ways: first, by quantifying in detail pathologist predictors of diagnostic performance in the UK, and, second, by developing machine learning tools for automated risk stratification of Barrett’s oesophagus. Within our web-based digital review environment we are implementing tools which automatically log slide coordinate positions, magnification (zoom) level, and time stamps as pathologists navigate case slides. These tracking data serve as a proxy for diagnostic behavior which, together with the overall diagnostic concordance data, will allow us to develop and train a convolutional neural network for automated diagnostic image analysis. We will validate our automated classifier on an independent set of expert-reviewed Barrett’s oesophagus cases. This innovative workflow optimally combines the advantages of trained pathologist input with objective deep learning data analysis. How the results will be used Using these machine learning resources, we will develop a novel computational tool that can facilitate patient diagnosis and risk stratification in routine histopathology. This will reduce unwanted variation and overdiagnosis of patients with precursor lesions of oesophageal cancer.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS