title: Patient specific image driven evaluation of the aggressiveness of metastases to the lung.
abstract: 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.
authors.forename: Thierry
authors.initials: T
authors.lastname: Colin
authors.forename: François
authors.initials: F
authors.lastname: Cornelis
authors.forename: Julien
authors.initials: J
authors.lastname: Jouganous
authors.forename: Marie
authors.initials: M
authors.lastname: Martin
authors.forename: Olivier
authors.initials: O
authors.lastname: Saut
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000503:physiopathology / Q000556:secondary*; D008954:Models, Biological; D018802:Patient-Centered Care; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*; D047368:Tumor Burden