Monc: Data-driven medicine against cancer

Coupling models with data to address relevant challenges for biologists and clinicians

The Inria team MONC is devoted to applications of mathematical modeling and machine learning in oncology with the following aims:

– Improve our understanding in cancer biology,
– Assist the development of novel therapeutic approaches,
– Develop personalized decision-helping tools for monitoring the disease and evaluating therapies.

More precisely, we aim at developing new mathematical models built from a precise biological and medical knowledge – combined with image processing, statistical methods and artificial intelligence – in order to build numerical tools based on available quantitative data about cancer.

The goal is finally to be able to help clinicians and/or biologists to better understand, predict or control tumor growth and possibly evaluate the therapeutic response, in a clinical context or for pre-clinical studies. We plan to develop patient-specific approaches (mainly based on medical imaging) as well as population-type approaches in order to take advantage of clinical cohorts.

Each type of cancer is different and only a limited number of pathologies are targeted among which: lung metastases, meningioma, gliomas, soft-tissue sarcoma, kidney, lung and liver tumors.
Recently, the team’s application interests have expanded towards muco-ciliary clearance involved in pulmonary diseases (Cystic fibrosis, chronic obstructive pulmonary disease).