MRI Based Bayesian Personalization of a Tumor Growth Model

Matthieu Lê, Hervé Delingette, Jayashree Kalpathy-Cramer, Elizabeth R. Gerstner, Tracy Batchelor, Jan Unkelbach, Nicholas Ayache

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

The mathematical modeling of brain tumor growth has been the topic of numerous research studies. Most of this work focuses on the reaction-diffusion model, which suggests that the diffusion coefficient and the proliferation rate can be related to clinically relevant information. However, estimating the parameters of the reaction-diffusion model is difficult because of the lack of identifiability of the parameters, the uncertainty in the tumor segmentations, and the model approximation, which cannot perfectly capture the complex dynamics of the tumor evolution. Our approach aims at analyzing the uncertainty in the patient specific parameters of a tumor growth model, by sampling from the posterior probability of the parameters knowing the magnetic resonance images of a given patient. The estimation of the posterior probability is based on: 1) a highly parallelized implementation of the reaction-diffusion equation using the Lattice Boltzmann Method (LBM), and 2) a high acceptance rate Monte Carlo technique called Gaussian Process Hamiltonian Monte Carlo (GPHMC). We compare this personalization approach with two commonly used methods based on the spherical asymptotic analysis of the reaction-diffusion model, and on a derivative-free optimization algorithm. We demonstrate the performance of the method on synthetic data, and on seven patients with a glioblastoma, the most aggressive primary brain tumor. This Bayesian personalization produces more informative results. In particular, it provides samples from the regions of interest and highlights the presence of several modes for some patients. In contrast, previous approaches based on optimization strategies fail to reveal the presence of different modes, and correlation between parameters.

Original languageEnglish (US)
Article number7463075
Pages (from-to)2329-2339
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume35
Issue number10
DOIs
StatePublished - Oct 1 2016
Externally publishedYes

Keywords

  • Bayesian
  • glioblastoma
  • LBM
  • monte carlo
  • personalization
  • reaction-diffusion
  • tumor growth

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'MRI Based Bayesian Personalization of a Tumor Growth Model'. Together they form a unique fingerprint.

  • Cite this

    Lê, M., Delingette, H., Kalpathy-Cramer, J., Gerstner, E. R., Batchelor, T., Unkelbach, J., & Ayache, N. (2016). MRI Based Bayesian Personalization of a Tumor Growth Model. IEEE Transactions on Medical Imaging, 35(10), 2329-2339. [7463075]. https://doi.org/10.1109/TMI.2016.2561098