Detection of Early Cancer with Deep Histopathological Analysis

An ACED Pilot Research Project

Detection of Early Cancer with Deep Histopathological Analysis

Lead 1: Prof. Rajan Kulkarni, OHSU

Lead 2: Prof. Richard Marais, University of Manchester



OHSU: Prof Sancy Leachman, Prof Young Hwan Chang, Dr Tracy Petrie

University of Manchester: Dr Nathalie Dhomen, Prof Martin Cook


Project Summary

The goal of this research is to develop augmented intelligence (AI) machine learning computational tools that will help us to analyse both tissue pathology slides and DNA changes (mutations) within the same tissues, to help determine whether a particular suspicious tissue is cancerous or has strong potential to become cancerous. Standard microscopic analyses by pathologists sometimes provides unclear information about the cancerous nature of a given tissue; however, machine learning analyses of the same tissues may provide additional information that could allow for appropriate management (ie additional surgery versus medicine versus observation). This pilot project will allow us to test the hypothesis that deep computational analysis of tissue slides coupled with mutational information can allow us to better detect cancer in otherwise equivocal or uncertain cases, which can then be scaled up in larger cohorts and with additional tissue types.


Research Themes

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About ACED Manchester

ACED is a £55 million partnership between world-leading early detection institutes and organisations dedicated to improving the early detection of cancer.

Funding Opportunities

Discover the opportunities for ACED Member organisations to lever additional ACED funding in strategic areas.