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

Vishal Patel,Leith Hathout

doi:10.1186/s12976-017-0056-7

Abstract

BackgroundThe heterogeneity of response to treatment in patients with glioblastoma multiforme suggests that the optimal therapeutic approach incorporates an individualized assessment of expected lesion progression. In this work, we develop a novel computational model for the proliferation and necrosis of glioblastoma multiforme.MethodsThe model parameters are selected based on the magnetic resonance imaging features of each tumor, and the proposed technique accounts for intrinsic cell division, tumor cell migration along white matter tracts, as well as central tumor necrosis. As a validation of this approach, tumor growth is simulated in the brain of a healthy adult volunteer using parameters derived from the imaging of a patient with glioblastoma multiforme. A mutual information metric is calculated between the simulated tumor profile and observed tumor.ResultsThe tumor progression profile generated by the proposed model is compared with those produced by existing models and with the actual observed tumor progression. Both qualitative and quantitative analyses show that the model introduced in this work replicates the observed progression of glioblastoma more accurately relative to prior techniques.ConclusionsThis image-driven model generates improved tumor progression profiles and may contribute to the development of more reliable prognostic estimates in patients with glioblastoma multiforme.

Highlights

The heterogeneity of response to treatment in patients with glioblastoma multiforme suggests that the optimal therapeutic approach incorporates an individualized assessment of expected lesion progression
Model construction We propose a model that combines the diffusion tensor driven migration of cells along white matter bundles (Eq 2) with logistic tumor cell proliferation and with a novel necrosis term which is activated once cell density has surpassed tissue supporting capacity:
We qualitatively describe the resulting tumor progression profiles in relation to the original, and we quantify the differences between profiles initialized with slightly different seeds using the mutual information metric as described above

Summary

Introduction

The heterogeneity of response to treatment in patients with glioblastoma multiforme suggests that the optimal therapeutic approach incorporates an individualized assessment of expected lesion progression. We develop a novel computational model for the proliferation and necrosis of glioblastoma multiforme. Glioblastoma multiforme (GBM), the most common primary brain neoplasm in adults, represents the most malignant end of the glioma spectrum. Though the prognosis is generally poor, there is considerable variability in response to treatment and patient outcomes. This variation suggests that the optimal therapeutic approach likely incorporates an individualized assessment of expected tumor progression. There has been considerable interest in developing computational models that accurately replicate the observed temporal evolution of GBM lesions.

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