Bayesian Personalization of Brain Tumor Growth Model

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.

What makes Data Science different? A discussion involving Statistics2.0 and Computational Sciences

“…Successful approaches have been recently published [19,47] which enable predictive science in Medicine, for example for lasertreatment of tumours [42]. The reader is referred to [47] for a recent discussion of the emerging field known as "ComputerGuided Predictive Medicine", to [52] for applications to brain tumour model personalisation and to [53] for sparse Bayesian image registration.…”

A Survey on Medical Image Segmentation Methods with Different Modalitites

“…The last depends on the inclination free improvement technique BOBYQA, and is utilized as a part of crafted by Konukoglu et al [58]. This paper expands [59] with an examination with a circular asymptotic investigation of the personalization, and more complete examination on 3 extra persons. Likewise, another probability demonstrate in view of the 95th percentile of the Hausdorff separate is utilized, and also another log uniform earlier for the parameters of intrigue.…”

“…To our information, this is the to begin with work that employments a personalized show of brain tumor development taking into account the instability in tumor development parameters and the clinician's segmentations in arrange to optimize radiotherapy arranging here they utilized a few strategies for division i.e., One time point is utilized to taking test from the back dissemination utilizing the Metropolis-Hasting calculation to begin with portrayed by [7], and utilized for tumor development personalization in [8]. Two time focuses strategy is utilized by Gaussian Prepare Hamiltonian Monte Carlo (GPHMC) calculation.…”

“…Likewise, another probability demonstrate in view of the 95th percentile of the Hausdorff separate is utilized, and also another log uniform earlier for the parameters of intrigue. At long last, the parameters of the GPHMC have been refreshed to expand the vigor of the personalization: I) the instatement of the Gaussian procedure is presently finished with a coarse framework to guarantee that the entire space of parameters is secured, ii) the parameters of the Gaussian procedure are set by boosting the probability, iii) the clamor level of the probability show has been diminished from σ = 10 mm in [59] to σ=5 mm in order to increment the emphasis on the locale of intrigue.…”

Spatially Adaptive Multi-Scale Optimization for Local Parameter Estimation in Cardiac Electrophysiology

“…These include derivative-free optimization [1], [4], [5], Bayesian estimation by building surrogates using Polynomial chaos [6] or Gaussian process [7], and Bayesian filtering [8]. Essentially, all these methods involve a non-intrusive, repeated evaluation of the physiological model.…”