Abstract
Breast cancer affects nearly 60,000 new women in France each year, with a net 5-year survival of 87%. However, for a breast cancer detected at an early stage, the 5-year survival is 99%. In an attempt to detect breast cancer in women at an early stage, France has set up a national screening program organized for women aged 50 to 74 and at "medium" risk. In 2017, French citizens and professionals have expressed their wish that the organized screening becomes more and more personalized according to risk factors. In the first part of this thesis, we developed TEMAS (Text-mining Algorithm- assisted Search), a new literature search method using text-mining and classification methods. We then created a web application allowing people to implement TEMAS without the need for special knowledge. In the second part of this thesis, given the risk factors identified with TEMAS, we developed a questionnaire. This questionnaire was completed by 3,077 women included in the organized screening in the Hérault department, France, in order to estimate their individual risk of breast cancer for a 12-year follow-up, based on a score. This score was constructed using a Cox model and the Breslow estimator. Then, within the population currently considered at "medium" risk, this score enabled to delineate two risk thresholds, on the one hand of lower risk, and on the other hand of higher risk. Then, according to the level of risk within the three groups of graduated risk, we proposed a new strategy that personalized the organized screening.