Preproject number 255241 The main project is all about researching and developing ICT solutions to supplement or replace methods in pathology to increase productivity and quality and hence treatment of cancer, based on analysis of Big Data produced by digital pathology. Most prognostic studies suffer from undersampling. To account for heterogeneity most studies would have to increase the workload by a factor of 5-10. With limited resources and strongly limited access to pathologists, this cannot be achieved unless we manage to digitalize and largely automate both the preparation and analysis required to render a diagnosis and identify prognostic biomarkers. This is what this project is designed to do, and we have put together a group of international leaders in the different fields involved, from robotics and machine learning to digital image analysis and from tumor pathology to cancer surgery and oncology. Our ambition is to completely transfer the very complex thinking and decision-making from its current basis in visual observation to a computer basis with objective, reproducible algorithms. The concepts involved are based on image analysis and more specifically: deep learning, texture analysis, and quantification of DNA. We will focus on three major cancer forms, i.e. lung cancer, colorectal cancer and prostate cancer and work with large retrospective clinical materials with known clinical outcomes and the methods will be applied to routine paraffin-imbedded material. The DoMore! project will facilitate the long awaited digitalization of pathology and establish more efficient and objective cancer prognostication that can be made equally available to all patients. We expect a number of different project results; increased efficiency in pathology, methods and markers to aid the clinician to give better and more personalized treatment to cancer patients, patents and publications, products (algorithms, applications, services, data) and spin-off companies.
Project leader: Håvard E. Danielsen
Category: Helse Sør-Øst RHF
Institution: Institutt for kreftgenetikk og informatikk