MICA: Developing Micro-Community Analytics for Histology Landscapes (MiCAHiL)

The current 'gold standard' for diagnosis and grading of many diseases (including most solid tumours) is largely based on an expert histopathologist's visual microscopic assessment of an extremely thin (only a few micrometers thick) section of the suspicious tissue specimen glued to a glass slide. This practice has remained more or less the same for several decades, and results in subjective and variable diagnosis. However, the recent uptake of digital slide scanners by some diagnostic pathology laboratories in the UK marks a new revolution in pathology practice in the NHS trusts, with our local NHS trust being the first one in the country to use digitally scanned images of tissue slides for routine diagnostics. The digital slide scanner produces a multi-gigapixel whole-slide image (WSI) for each histology slide, with each image containing rich information about tens of thousands of different kinds of cells and their spatial relationships with each other. This project aims to introduce a novel paradigm for analytics and computerised profiling of tissue microenvironment. We will develop sophisticated tools for image analytics in order to reveal spatial trends and patterns associated with disease sub-groups (for example, patient groups whose cancer is likely to advance more aggressively) and deploy those tools for clinical validation at our local NHS trust. This will be made possible by further advancing recent developments made in our group, such as those allowing us to recognise individual cells of different kinds in the WSIs consequently enabling us to paint a colourful picture of the tissue microenvironment which we term as the 'histology landscape'. Understanding and analysing the tissue microenvironment is not only crucial to assessing the grade and aggressiveness of disease and for predicting its course, it can also help us better understand how genomic alterations manifest themselves as structural changes in the tissue microenvironment. We will develop tools and techniques to extract patterns and trends found in the spatial structure and the 'social' interplay of different cells or colonies of cells found in the complex histology landscapes. Our goal is to establish the effective use of image analytics for understanding the histology landscape in a quantitative and systematic manner, facilitating the discovery of image-based markers of disease progression and survival that are intuitive, biologically meaningful, and clinically relevant - eventually leading to optimal selection of treatment option(s) customised to individual patients. This project will analyse real image data and associated clinical and genomics data from patient cohorts for colorectal cancer as a case study. The research staff on this project will work closely with clinical collaborators to ensure the biological significance and clinical relevance of spatial trends and patterns found in the data. In collaboration with our industrial partner Intel, we will test and demonstrate the effectiveness of our methods in a clinical setting potentially leading to better healthcare provision for patients and potential cost savings for the NHS.