Patient specific image driven evaluation of the aggressiveness of metastases to the lung.

Metastases to the lung are a therapeutic challenge because some are fast-evolving while others evolve slowly. Any insight that can be provided for which nodule has to be treated first would help clinicians. In this work, we evaluate the aggressiveness but also the response to treatment of these nodules using a calibrated mathematical model. This model is a macroscopic model describing tumoral growth through a set of nonlinear partial differential equations. It has to be calibrated to a specific patient and a specific nodule using a temporal sequence of CT scans. To this end, a new optimization technique based on a reduced order method is developed. Finally, results on two clinical cases are presented that give satisfactory numerical prognosis of the evolution of a nodule during different phases: growth, treatment and post-treatment relapse.