Seminar "Tissue-scale, patient-specific computational modeling and simulation of prostate cancer growth"
Salon de Grados, Edificio Politécnico
Miércoles 28 de Noviembre del 2018
Grupo de Oncología Matemática

Tissue-scale, patient-specific computational modeling and simulation of prostate cancer growth

Guillermo Lorenzo

Dipartimento di Ingegneria Civile ed Architettura, Università degli Studi di Pavia, Italy

Salon de Grados, Edificio Politécnico, 11:30 horas

Recently, computational technologies to simulate cancer growth and treatments have enabled the patient-specific prediction of clinical outcomes and the design of optimal therapies. This new trend in medical research has been termed mathematical oncology.

Prostate cancer is a major health problem among aging men worldwide and an ideal candidate to benefit from this approach. The current medical guidelines of prostate cancer largely rely on a statistical and experiential basis that limits the personalization of the clinical management of the disease, so many patients are overtreated or undertreated. The small size of the prostate and the advent of powerful and versatile computational technologies enable the organ-scale simulation of the disease based on imaging and clinical data. Additionally, many patients are closely monitored throughout periodic imaging and clinical tests before treatment, opening the door for in vivo model validation.

We propose a computational framework for the tissue-scale, patient-specific modeling and simulation of organ-confined prostate cancer growth within the context of mathematical oncology. We present mathematical models to describe the evolution of prostatic tumors based on key biological and mechanical phenomena and we simulate these models in experimental setups and in tissue-scale, patient-specific scenarios. We leverage isogeometric analysis to handle the nonlinearity of our models, as well as the complex anatomy of the prostate and the intricate tumoral morphologies. We further advocate dynamical mesh adaptivity to speed up calculations, rationalize computational resources, and facilitate simulation in a clinically relevant time.