title: A generative approach for image-based modeling of tumor growth.
abstract: Extensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from different image sources and different time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multimodal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA.
authors.forename: Bjoern H
authors.initials: BH
authors.lastname: Menze
authors.forename: Koen
authors.initials: K
authors.lastname: Van Leemput
authors.forename: Antti
authors.initials: A
authors.lastname: Honkela
authors.forename: Ender
authors.initials: E
authors.lastname: Konukoglu
authors.forename: Marc-André
authors.initials: MA
authors.lastname: Weber
authors.forename: Nicholas
authors.initials: N
authors.lastname: Ayache
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000473:pathology*; D049109:Cell Proliferation; D003198:Computer Simulation; D005910:Glioma / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008954:Models, Biological; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity