CentraleSupélec, with the collaboration of INSERM and Université Paris-Saclay, has joined forces with the Institut Gustave Roussy to launch the Centre National de Précision en oncologie, PRISM. The mission of this second-generation precision medicine centre will be to model cancer on an individual scale by creating numerical avatars of tumours. The aim is to identify patients with the most aggressive cancers very early in the disease, without waiting for relapses, in order to offer them the most appropriate treatment from the start of treatment, using the huge volume of clinical, biological and molecular data and their analysis by artificial intelligence. PRISM will conduct large-scale clinical studies and develop molecular analysis technologies and data analysis methods.
Coordinated by Professor Fabrice André, Research Director of Gustave Roussy, Inserm Research Director and Professor at Paris-Saclay University, Prism aims to revolutionize the understanding of the molecular and biological mechanisms of cancer development and progression through artificial intelligence. Based on increasingly rich data of various types (clinical, genomic, microbiological, imaging, etc.), learning algorithms make it possible to develop finer diagnostic and prognostic tools, and thus to propose therapies that are personalised according to the characteristics of the individual.
Funded by the French National Research Agency, Prism received the IHU label in 2018, followed by the National Center for Precision Medicine label.
Two laboratories of the School are involved in this project:
- MICS laboratory (Mathematics and Computer Science for Complexity and Systems) which has worked on new methods and new reasoning for data processing via algorithms. The variety and the immensity of the volumes of data collected being too important in terms of human processing capacity, this laboratory therefore offers the help of AI solutions that know how to sort, analyse and interpret all medical data.
- CVN laboratory (Centre for Visual Computing) is mainly involved in medical imaging and radiology, thanks in particular to the use of deep learning.
