Parameter generation for the patient-specific biomathematical model.

<p><b>1.</b> Determine radial measurements from serial T1Gd and T2/FLAIR magnetic resonance imaging. <b>2.</b> Compute the invisibility index (D/<i>ρ</i>) from intra-study T1Gd and T2/FLAIR radial measurements. <b>3.</b> Compute the radial velocity () from serial T1Gd or T2/FLAIR radial measurements.</p

Optimization Restrictions.

*<p>Patient-specific.</p

Dose-volume histogram showing the total dose delivered to normal brain tissue outside of the T1Gd abnormality for optimized and standard-of-care plans for patient 10.

<p>The lines intersect at 62% of the normal tissue volume, demonstrating that only 2% of the normal tissue receives a higher dose from the optimized plan.</p

Patient Data.

*<p>Extent of Resection.</p>†<p>Still alive.</p>‡<p>Biopsy only.</p>¶<p>Temozolomide.</p>#<p>Subtotal resection.</p>||<p>Carmustine.</p>§<p>Not included in Rockne 2010.</p

Simulated tumor and normal cell densities with clinical and optimized total dose.

<p>Patients are ordered according to tumor diffusivity, from least to greatest and the cell densities are taken at the pre-treatment timepoint. Dose in units Gray is on the left axis while cell density relative to the tumor cell carrying capacity is on the right side. The spatial distribution of the optimized plans is determined primarily by the patient-specific invisibility index (<i>D/ρ</i>), as patients with more nodular tumors (low <i>D/ρ</i>, e.g. Patient 12) receive more peaked optimized doses while those for patients with more diffuse tumors (high <i>D/ρ</i>, e.g. Patient 3) are more spread out along the invasive gradient of the outer edge of the tumor. Patients 2 and 5 show a cell density of zero in the center of the tumor due to subtotal resections.</p

Spatial distribution of optimized plans versus the invisibility index.

<p>The radial distance between the 50% isodose radius and the 50% tumor cell isodensity radius versus the invisibility index (<i>D/ρ)</i> for the optimized plans. The horizontal error bars illustrate the range of patient-specific D/p values possible given the observed uncertainty in radial tumor measurements. The vertical error bars represent the minimum and maximum distance from simulations using the expected, minimum and maximum D/p values. The marker is plotted in the center of the range and the center values are positively correlated with Pearson’s correlation r = .98 with p-value = 9e-8, demonstrating that tumors with higher invisibility indices receive optimized doses with shallower gradients and larger high-dose volumes relative to tumor cell density.</p

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

<div><p>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.</p> </div

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

<p>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.</p

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

<p><i>a</i>: 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. <i>b</i>: 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.</p

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

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