A 3-dimensional DTI MRI-based model of GBM growth and response to radiation therapy

Abstract

Abstract. Glioblastoma (GBM) is both the most common and the most aggressive intra-axial brain tumor, with a notoriously poor prognosis. To improve this prognosis, it is necessary to understand the dynamics of GBM growth, response to treatment and recurrence. The present study presents a mathematical diffusion-proliferation model of GBM growth and response to radiation therapy based on diffusion tensor (DTI) MRI imaging. This represents an important advance because it allows 3-dimensional tumor modeling in the anatomical context of the brain. Specifically, tumor infiltration is guided by the direction of the white matter tracts along which glioma cells infiltrate. This provides the potential to model different tumor growth patterns based on location within the brain, and to simulate the tumor's response to different radiation therapy regimens. Tumor infiltration across the corpus callosum is simulated in biologically accurate time frames. The response to radiation therapy, including changes in cell density gradients and how these compare across different radiation fractionation protocols, can be rendered. Also, the model can estimate the amount of subthreshold tumor which has extended beyond the visible MR imaging margins. When combined with the ability of being able to estimate the biological parameters of invasiveness and proliferation of a particular GBM from serial MRI scans, it is shown that the model has potential to simulate realistic tumor growth, response and recurrence patterns in individual patients. To the best of our knowledge, this is the first presentation of a DTI-based GBM growth and radiation therapy treatment model.

On PTV definition for glioblastoma based on fiber tracking of diffusion tensor imaging data

“…DTI data have been used successfully in the context of glioma evaluation in several regards. For example, they have been used to adapt target volume definitions for GBM treatment planning [24][25][26], for modeling of brain tumor growth [27][28][29][30], to detect early malignant transformation of low-grade glioma [31], to differentiate between GBM and brain metastases [32], to use DTI-derived fiber tracking in surgery planning [33], and to detect the infiltration of the corpus callosum [34]. Our inability to properly connect most primary and secondary tumors does not diminish the value of DTI for these applications.…”

Image-driven modeling of the proliferation and necrosis of glioblastoma multiforme

“…Each of these greatly complicate the understanding of the progression of an individual cell population, which we provide the fundamental groundwork for in the present work. The most practically useful models will of course need to account for these ongoing treatment factors, one of which – radiation therapy – we have modeled separately elsewhere [24]. Specifically, we note that we have considered D to be time-invariant, and ρ and η to be constant across both time and space.…”

Is Diffusion Tensor Imaging-Guided Radiotherapy the New State-of-the-Art? A Review of the Current Literature and Technical Insights

“…Some studies have used the whole tensor to predict tumour spread. An article used a 3D-based DTI model that uses the fibre orientation to predict glioma growth [25]. However, this study did not evaluate the model accuracy quantitatively.…”