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.

Figure 1. Growth of an untreated virtual control (UVC) tumor for a 57 year-old patient with a left fronto-parietal lobe glioblastoma.

a: Volumetric images of the untreated virtual control at six time points where Day 0 is the time of the patient's first pre-treatment MRI scan. Pseudocoloring indicates the tumor cell density, normalized to the maximum cell density of the tissue. b: T1Gd spherically-equivalent radius time curve from same simulation showing how the Days Gained score is computed. c: Post-treatment T1Gd MRI slice showing actual tumor (red outline) and perimeter of the simulated tumor's T1Gd-enhancing region at the same time point (cyan outline). Image oriented according to radiological convention: patient left is on the right.

Figure 2. Comparisons between T1Gd MRI data and untreated virtual control (UVC) prediction at post-treatment time point.

Patient was 58 years old and underwent biopsy followed by conformal radiation therapy with concurrent temozolomide chemotherapy. Top row: MRI data. Middle row: Actual tumor perimeter (red) with superimposed UVC-predicted tumor perimeter (cyan). Bottom row: full distribution of UVC cell densities showing invasion peripheral to abnormality. Outermost blue cell density profile represents a very low, but non-zero, threshold. Perimeter of actual tumor outlined in white.

Figure 3. Spatial comparisons between baseline pre-treatment MRI (red outline) and simulation results (cyan outline) seeded with our initial condition. a: large tumor on T1Gd MRI.

b: smaller tumor on FLAIR MRI. c: Highly anisotropic tumor growth on T2-weighted MRI. Our simulations produce tight spatial matches to a range of tumors (a and b), but decrease in accuracy for tumors with high anisotropy (c).

Figure 4. Kaplan-Meier analyses on progression-free and overall survival.

a: Analysis on progression-free survival data revealed a significant difference between the patients with Days Gained scores greater than or equal to 100 and those with lower scores. b: Overall survival analysis also revealed a significant difference between the patients with Days Gained scores greater than or equal to 117 and those with lower scores.