Discriminating Survival Outcomes in Patients with Glioblastoma Using a Simulation-Based, Patient-Specific Response Metric

Abstract

Accurate clinical assessment of a patient's response to treatment for glioblastoma multiforme (GBM), the most malignant type of primary brain tumor, is undermined by the wide patient-to-patient variability in GBM dynamics and responsiveness to therapy. Using computational models that account for the unique geometry and kinetics of individual patients' tumors, we developed a method for assessing treatment response that discriminates progression-free and overall survival following therapy for GBM. Applying these models as untreated virtual controls, we generate a patient-specific “Days Gained” response metric that estimates the number of days a therapy delayed imageable tumor progression. We assessed treatment response in terms of Days Gained scores for 33 patients at the time of their first MRI scan following first-line radiation therapy. Based on Kaplan-Meier analyses, patients with Days Gained scores of 100 or more had improved progression-free survival, and patients with scores of 117 or more had improved overall survival. Our results demonstrate that the Days Gained response metric calculated at the routinely acquired first post-radiation treatment time point provides prognostic information regarding progression and survival outcomes. Applied prospectively, our model-based approach has the potential to improve GBM treatment by accounting for patient-to-patient heterogeneity in GBM dynamics and responses to therapy.

Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma

“…What is more, from the double thresholding technique we used on the k trans image, the BU-NMF hypoxic regions best correlated to k trans image with values between 0.15 and 0.76 approximately, well-perfused areas to k trans above 0.76 and necrotic regions to k trans below 0.15. These results are in agreement with published results and findings on tumor hypoxia [16,25] as explained in Section 3.3. The histopathological findings indicated the presence of necrotic and well-perfused areas in all three lesions, however the detection of hypoxic regions would need further analysis since we only obtained immunohistochemistry results for one of the three patients.…”

“…From this, we could assume that the necrotic image regions according to our classification exhibit k trans values lower than 0.15, wellperfused exhibit k trans more than 0.76 and hypoxic regions lie in between these two thresholds. This assumption is reasonable since the hypoxic k trans range is in line with findings claiming that the bulk of the tumor consists mainly of hypoxia; hence, the periphery of the tumor, which typically relates to the normoxic/well-perfused area, occupies smaller space [16,25]. Necrotic regions instead are those with k trans close to zero, occupying a smaller space with respect to hypoxia.…”

The biology and mathematical modelling of glioma invasion: a review

“…While their ability to probe into specific outcomes from particular changes in, say, a signalling pathway is limited, phenomenological models are better able to provide insights into long-term, large-scale behaviour than models focused on smaller-scale phenomena. Indeed, phenomenological models have proven useful in characterizing glioma invasion, identifying glioma patients receiving maximal benefit from therapeutic interventions [177] and defining a more prognostic response metric for patients than is currently available [178,179].…”

3D Mathematical modeling of glioblastoma suggests that transdifferentiated vascular endothelial cells promote resistance to current standard-of-care therapy.

“…See recent review articles for further details and references (26)(27)(28). Because of their simplicity, continuum reaction-diffusion equations have been widely used to describe the infiltration of GBM cells in the brain (29)(30)(31) and to develop patient-specific therapeutic approaches (32,33). Here, we use a continuum-level multiscale, mixture type model, extending previous work (4, 7) to simulate the dynamics of GBM stem, GBM progenitor, transdifferentiated endothelial cells, terminally differentiated, dead cells (DC).…”