Predicting the Location of Glioma Recurrence after a Resection Surgery

Stretton, Erin; Mandonnet, Emmanuel; Geremia, Ezequiel; Menze, Bjoern; Delingette, Hervé; Ayache, Nicholas

doi:10.1007/978-3-642-33555-6_10

Cited by 5 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, for Patient B, this ratio raises from 6.45 to 47.70 as its shape evolves from a "sphere-like" to a "star-like" (Fig 9). Interestingly, in a recent paper [32], a different ratio (d w /ρ) was used to quantify the diffusivity of the tumor boundary where a low white matter diffusivity rate over proliferation ratio indicates a not very infiltrative glioma (bulky), whereas high ratio defines diffuse tumor. The main objective was to determine whether the tumor recurrence is a bulky or diffuse-type recurring tumor.…”

Section: Discussionmentioning

confidence: 99%

“…DT-MRI serves as the basis for several reaction-diffusion based models to extrapolate glioma invasion margins for radiation [33] and after resection [32] using at least two acquired time points. Those two studies have been successively evaluated on one healthy subject and one LGG patient, and they confirm that the importance of using high resolution and high quality DT-MRI which is obviously a strong constraint.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Tumor growth parameters estimation and source localization from a unique time point: Application to low-grade gliomas

Rekik

Allassonnière

Clatz

et al. 2013

Computer Vision and Image Understanding

Self Cite

View full text Add to dashboard Cite

Coupling time series of MR Images with reaction-diffusion-based models has provided interesting ways to better understand the proliferative-invasive aspect of glial cells in tumors. In this paper, we address a different formulation of the inverse problem: from a single time point image of a non-swollen brain tumor, estimate the tumor source location and the diffusivity ratio between white and grey matter, while exploring the possibility to predict the further extent of the observed tumor at later time points in low-grade gliomas. The synthetic and clinical results show the stability of the located source and its varying distance from the tumor barycenter and how the estimated ratio controls the spikiness of the tumor.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Tumor growth parameters estimation and source localization from a unique time point: Application to low-grade gliomas

Rekik

Allassonnière

Clatz

et al. 2013

Computer Vision and Image Understanding

Self Cite

View full text Add to dashboard Cite

show abstract

“…This representation is used in this paper for the no-DTI case, where the isotropic and homogeneous growth in white matter (WM) is guided by a WM segmentation (Figure 1(c)). Other researchers [2,4,7,8] have examined anisotropic diffusivity where D(x) is d g I if x is in gray matter and d w D water if x is in white matter, where D water is the water diffusivity tensor obtained from a DTI normalized using the maximum eigenvalue in white matter [2]. These D water based DTIs are used for the patient-DTI and atlas-DTI cases.…”

Section: Tensor Forms For Three Dti Optionsmentioning

confidence: 99%

Importance of patient DTI's to accurately model glioma growth using the reaction diffusion equation

Stretton

Geremia

Menze

et al. 2013

2013 IEEE 10th International Symposium on Biomedical Imaging

View full text Add to dashboard Cite

Tumor growth models based on the FisherKolmogorov reactiondiffusion equation (FK) have shown convincing results in reproducing and predicting the invasion patterns of gliomas brain tumors. Diffusion tensor images (DTIs) were suggested to model the anisotropic diffusion of tumor cells in the brain white matter. However, clinical patient-DTIs are expensive and often acquired with low resolution, which compromises the accuracy of the tumor growth models. In this work, we used the traveling wave approximation model to describe the evolution of the visible boundary of the tumor modeled by the FK equation to investigate the impact of replacing the patient DTI by (i) an isotropic diffusion map or (ii) an anisotropic high-resolution DTI atlas formed by averaging DTIs of multiple patients. We quantify the impact of replacing the patient DTI using three metrics: the shape of the simulated glioma, the estimation of the tumor growth parameters, and the prediction performance on clinical cases.

show abstract

“…Second, the personalization of such models to specific patients could allow one to quantify the aggressiveness of the tumor, which has been shown to be correlated with clinically relevant information [2], [3]. Third, personalized models could help predicting the future evolution of a given tumor [4]. Fourth, personalized models could lead the way toward objective and more efficient personalized therapy.…”

Section: Introduction a Motivationsmentioning

confidence: 99%

MRI Based Bayesian Personalization of a Tumor Growth Model

Le¹,

Delingette²,

Kalpathy–Cramer

et al. 2016

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Predicting the Location of Glioma Recurrence after a Resection Surgery

Cited by 5 publications

References 17 publications

Tumor growth parameters estimation and source localization from a unique time point: Application to low-grade gliomas

Tumor growth parameters estimation and source localization from a unique time point: Application to low-grade gliomas

Importance of patient DTI's to accurately model glioma growth using the reaction diffusion equation

MRI Based Bayesian Personalization of a Tumor Growth Model

Contact Info

Product

Resources

About