Bayesian personalization of brain tumor growth model

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Recent work on brain tumor growth modeling for glioblastoma using reaction-diffusion equations suggests that the diffusion coefficient and the proliferation rate can be related to clinically relevant information. However, estimating these parameters is difficult due to the lack of identifiability of the parameters, the uncertainty in the tumor segmentations, and the model approximation, which cannot perfectly capture the dynamics of the tumor. Therefore, we propose a method for conducting the Bayesian personalization of the tumor growth model parameters. Our approach estimates the posterior probability of the parameters, and allows the analysis of the parameters correlations and uncertainty. Moreover, this method provides a way to compute the evidence of a model, which is a mathematically sound way of assessing the validity of different model hypotheses. Our approach is based on a highly parallelized implementation of the reaction-diffusion equation, and the Gaussian Process Hamiltonian Monte Carlo (GPHMC), a high acceptance rate Monte Carlo technique. We demonstrate our method on synthetic data, and four glioblastoma patients. This promising approach shows that the infiltration is better captured by the model compared to the speed of growth.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages424-432
Number of pages9
Volume9350
ISBN (Print)9783319245706, 9783319245706, 9783319245706
DOIs
StatePublished - 2015
Externally publishedYes
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: Oct 5 2015Oct 9 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9350
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period10/5/1510/9/15

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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    Lê, M., Delingette, H., Kalpathy-Cramer, J., Gerstner, E. R., Batchelor, T., Unkelbach, J., & Ayache, N. (2015). Bayesian personalization of brain tumor growth model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9350, pp. 424-432). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9350). Springer Verlag. https://doi.org/10.1007/978-3-319-24571-3_51