25 - "In Silico modelling of tumour prognosis: towards the digital twin"
Host:
Maria Angeles Perez Anson
Professor Mechanical Engineering Department.
Associate Director of the Engineering Research Institute of Aragon (I3A)
University of Zaragoza
Short Bio:
Mariangeles obtained her PhD in Computational mechanics (2004), she was a postdoctoral fellow at the Trinity College Dublin (Ireland) and at the Ecole de technologie supérieure Montreal (Canada). She is member of the European Society of Biomechanics since 2007 and past president (2018-2020). Her research and collaborations mainly focuses on computational biomechanics and mechanobiology, design of prostheses and implants, experimental and computational tissue engineering. She is author of more than 50 publications and more than 150 contributions to International and National Conferences.
ABSTRACT:
Cancer is a complex heterogenous disease due to the diversity of its underlying biological interactions and its high degree of genomic diversity. Insilico research has an enormous potential in contributing to deliver personalised diagnosis, prognosis and treatment for cancer. PRIMAGE project framework (PRedictive Insilico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers - grant agreement No. 826494) proposes an open cloud-based platform to support decision making in the clinical management of two paediatric cancers, Neuroblastoma (NB), the most frequent solid cancer of early childhood, and the Diffuse Intrinsic Pontine Glioma (DIPG). Our role is to deliver in-silico models of solid tumour growth for NB and DIPG using multiscale simulation frameworks to couple model at subcellular scale, to cell, to tissue and to complete organ models, enabling assessment of a radiotherapy and chemotherapy treatment on tumour progression for a given patient. Our in silico model will be the starting point for the creation of a digital twin incorporating patient-specific data, anatomical magnetic resonance imaging and combined with tumour growth models that may improve the treatments prognosis.
