Description of the project
Digital Pathology is currently regarded as one of the most promising avenues of diagnostic medicine. With the recent advent of Whole-Slide Imaging (WSI), the field of digital pathology produces daily a massive amount of images with related metadata (e.g. patient information, diagnosis, treatment). In the context of colon cancer, such images could be used for both diagnosis and to find some prognostic biomarkers. For example immune infiltrate are associated with better prognosis while high stromal contain or tumor budding or poor differentiation status are associated with poorer outcome. Making these quantitative analysis is time consuming for pathologist and frequently lack of reproducibility. To assist experts, automatic analysis of whole-slide images (WSI) has been recently studied to predict survival outcomes or making tumor classification.
The AiCOLO project aims to contribute to the development of new artificial intelligence techniques trained on a large cohort of clinical annotated colon cancer patients with a twofold objective.
On the one hand, we will develop an innovative prognostic aid tool to automatically classify tissues constituting WSI pathological slides and to enumerate the various known prognostic markers like TILS immune infiltration, stromal contain or eosinophil count in the different areas. The method will be trained to find image patterns in tumor tissue related to patients’ outcome.
On the other hand, we will also propose a resolutely new approach to predict RAS and BRAF genetic status from WSI. We aim to determine if artificial intelligence could detect patterns associated with such genetic features and could outperform clinical or immune infiltrate variables. The idea is to study the activation layers of a deep network trained to classify the patients in order to extract information to explain its decision.