Mathematical Modeling and Prediction of Clinical Metastasis - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year :

Mathematical Modeling and Prediction of Clinical Metastasis

Abstract

In the majority of cancers, secondary tumors (metastases) and associated complications are the main cause of death. To design the best therapy for a given patient, one of the major current challenge is to estimate, at diagnosis, the burden of invisible metastases and the future time of emergence of these, as well as their growth speed. In this talk, I will present the current state of our research efforts towards the establishment of a predictive computational tool for this aim. I will first shortly present the model used, which is based on a physiologically-structured partial differential equation for the time dynamics of the population of metastases, combined to a nonlinear mixed-effects model for statistical representation of the parameters’ distribution in the population. Then, I will show results about the descriptive power of the model on data from clinically relevant ortho-surgical animal models of metastasis (breast and kidney tumors). The main part of my talk will further be devoted to the translation of this modeling approach toward the clinical reality. Using clinical imaging data of brain metastasis from non-small cell lung cancer, several biological processes will be investigated to establish a minimal and biologically realistic model able to describe the data. Integration of this model into a biostatistical approach for individualized prediction of the model’s parameters from data only available at diagnosis will also be discussed. Together, these results represent a step forward towards the integration of mathematical modeling as a predictive tool for personalized medicine in oncology
Fichier principal
Vignette du fichier
11_metastasis_oaxaca.pdf (52.77 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01969108 , version 1 (03-01-2019)

Identifiers

  • HAL Id : hal-01969108 , version 1

Cite

Sébastien Benzekry. Mathematical Modeling and Prediction of Clinical Metastasis. Mathematical Challenges in the Analysis of Continuum Models for Cancer Growth, Evolution and Therapy, Nov 2018, Oaxaca, Mexico. ⟨hal-01969108⟩
108 View
102 Download

Share

Gmail Facebook Twitter LinkedIn More