UNmasking the Invisible Cancer

Within this project we will develop artificial intelligence (AI) methods to refine diffuse-type gastric cancer (DGC) diagnostics. Since DGC may be easily missed or hard to find on biopsies and prophylactic hereditary gastrectomy specimens, AI will aid the pathologist in the diagnostic work-up, improving the detection of relevant cell types among a very large set of slides, with high potential to improve cancer diagnostics. Furthermore, the automation in cell detection provided by AI algorithms will allow to quantitatively and objectively assess DGC patterns in large series of slides, potentially giving new insights in specific morphological features of DGC, such as patterns of spatial cell distributions.

With this project, researchers aim at using AI to better identify and classify future patients with (hereditary) DGC, to increase detection of individual patients and families, that might eventually result in better patient stratification for therapeutic options and clinical decisions. This will give more insight into specific features of CDH1 mutated DGC, both in a hereditary as well as sporadic setting. In the end, researchers aim to give public access to developed AI technology for research purposes.

Funding

Hanarth fonds

People

Chella van der Post

Chella van der Post

Pathologist

Radboudumc

Francesco Ciompi

Francesco Ciompi

Associate Professor

Iris Nagtegaal

Iris Nagtegaal

Pathologist

Pathology, Radboudumc

Robin Lomans

Robin Lomans

PhD Candidate