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
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.
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.