By Olivier Saut
The main goal of this talk is to present examples of how mathematical modeling and AI may help clinicians following the evolution of solid cancer. As the main insight on the disease is obtained from routine radiological images, our models are image-driven. When longitudinal data is available (as standard follow-up), mechanistic models based on PDE may be built and offer useful predictions of the evolution of the disease. I will present the case of meningioma and the derivation of an accurate model for the description of their natural growths. For patient-specific simulations, models have to be personalized which adds an additional challenge: data assimilation. I will described some novel methods that we have developed to recover model parameters from radiological history of patients and eventually build decision-helping algorithms. In some other cases, a single exam is available in routine for each patient. In such cases mechanistic models tend to be replaced by AI algorithms (which could also be used for automatic segmentations of lesions). I will present how we hope to combine these two approaches to reach better prediction abilities.
By Richard Bailleul
By Florence Hubert
By Pierre-Emmanuel Jabin
By Markus Schmidtchen
By Camille Pouchol
By Monika Joanna Piotrowska
By Annabelle Collin
By Sébastien Benzekry
By Jose Munoz
By Pasquale Ciarletta
By Olivier Saut
By Anna Marciniak-Czochra
By Aleksandra Ardaseva
By Kevin Painter
By Nadia Loy
By Min Tang
By Christian Schmeiser
By Thierry Goudon
By Ariane Trescases
By Monica Colpi
By Albert Schwarz
