In the majority of cancers, secondary tumors (metastases) and associated complications are the main cause of death. To design the best therapy for a given patient, one of the major current challenge is to estimate, at diagnosis, the eventual burden of invisible metastases and the future time of emergence of these, as well as their growth speed. In this poster, we recapitulate results towards this aim using a mechanistic model based based on a physiologically-structured partial differential equation for the time dynamics of the population of metastases, combined to a nonlinear mixed-effects model for statistical representation of the parameters’ distribution in the population. Results are presented about the descriptive power of the model on data from clinically relevant ortho-surgical animal models of metastasis (breast and kidney tumors). Then the translation of this modeling approach toward the clinical reality is investigated. Using clinical imaging data of brain metastasis from non-small cell lung cancer, several biological processes were investigated to establish a minimal and biologically realistic model able to describe the data. Integration of this model into a biostatistical approach for individualized prediction of the model’s parameters from data only available at diagnosis is finally presented. This mechanistic approach is compared to biologically agnostic models based on statistical tools such as Cox regression or machine learning algorithms.Together, these results represent a step forward towards the integration of mathematical modeling as a predictive tool for personalized medicine in oncology
