Investigating biomarker heterogeneity on colorectal cancer whole slide images by using tissue and cell types detected using Deep Learning
Aim
We are working on quantifying a variety of biomarkers on H&E-stained whole slide images of colorectal cancers by automatically detecting tissue- and cell types using deep learning models. In particular, we are interested in the distribution and heterogeneity of these biomarkers and the relationship of such heterogeneity with patient outcome, response, and other clinicopathological characteristics. Placing 2nd in the 2022 CoNiC Challenge, we, together with collaborators from Charité Berlin, developed a state-of-the-art nuclei segmentation and classification model with a variety of potential applications and are investigating its use cases.
Figure 1: Examples of colorectal cancer whole slide images with overlaid nuclei detections. Colors represent associated classes.
Beyond H&E based data, we are also investigating the immune response in colorectal cancer and the distribution of specific types using RNA Sequencing data as well as multiplex immunofluorescence images, with special focus on linking these features back to morphology and histology on H&E images.
Figure 2: On H&E it is already possible to detect many different cell types, including many immune cell populations. The spatial distribution of these different cell types shows obvious and less obvious patterns that may give more insight in the tumor – tumor microenvironment interaction.
Members
Elias Baumann
Inti Zlobec
María Rodríguez Martínez
Funding source