Simulation of patient-specific glioma models for therapy planning

Abstract : Tumor growth models based on the Fisher Kolmogorov (FK) reaction-diffusion equation have shown convincing results in reproducing and predicting the invasion patterns of glioma brain tumors. In this thesis we use different FK model formulations to i) assess the need of patient-specific DTIs when modeling LGGs, ii) study cancer cell infiltration after tumor resections, and iii) define a metric to determine progressive disease for low-grade glimoas (LGG).Diffusion tensor images (DTIs) have been suggested to model the anisotropic diffusion of tumor cells in brain white matter. However, patient specific DTIs are expensive and often acquired with low resolution, which compromises the accuracy of the tumor growth models' results. We used a FK formulation to describe the evolution of the visible boundary of the tumor 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 the DTIs of multiple patients. We quantify the impact of replacing the patient DTI using synthetic tumor growth simulations and tumor evolution predictions on a clinical case. This study suggests that modeling glioma growth with tissue based differential motility (not using a DTI) yields slightly less accurate results than using a DTI. However, refraining from using a DTI would be sufficient in situations when modeling LGGs. Therefore, any of these DTI options are valid to use in a FK formulation to model LGG growth with the purpose of aiding clinicians in therapy planning.After a brain resection medical professionals want to know what the best type of follow-up treatment would be for a particular patient, i.e., chemotherapy for diffuse tumors or a second resection after a given amount of time for bulky tumors. We propose a thorough method to leverage FK reaction-diffusion glioma growth models on post-operative cases showing brain distortions to estimate tumor cell infiltration beyond the visible boundaries in FLAIR MRIs. Our method addresses two modeling challenges: i) the challenge of brain parenchyma movement after surgery with a non-linear registration technique and ii) the challenge of incomplete post-operative tumor segmentations by combining two infiltration maps, where one was simulated from a pre-operative image and one estimated from a post-operative image. We used the data of two patients with LGG to demonstrate the effectiveness of the proposed three-step method. We believe that our proposed method could help clinicians anticipate tumor regrowth after a resection and better characterize the radiological non-visible infiltrative extent of a tumor to plan therapy.For LGGs captured on FLAIR/T2 MRIs, there is a substantial amount debate on selecting a definite threshold for size-based metrics to determine progressive disease (PD) and it is still an open item for the Response Assessment in Neuro-Oncology (RANO) Working Group. We propose an approach to assess PD of LGG using tumor growth speed estimates from a FK formulation that takes into consideration irregularities in tumor shape, differences in growth speed between gray matter and white matter, and volumetric changes. Using the FLAIR MRIs of nine patients we compare the PD estimates of our proposed approach to i) the ones calculated using 1D, 2D, and 3D manual tumor growth speed estimates and ii) the ones calculated using a set of well-established size-based criteria (RECIST, Macdonald, and RANO). We conclude from our comparison results that our proposed approach is promising for assessing PD of LGG from a limited number of MRI scans. It is our hope that this model's tumor growth speed estimates could one day be used as another parameter in clinical therapy planning.
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Erin Stretton. Simulation of patient-specific glioma models for therapy planning. Modeling and Simulation. Ecole Nationale Supérieure des Mines de Paris, 2014. English. ⟨NNT : 2014ENMP0064⟩. ⟨tel-01144425⟩



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