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

Maxwell Lewis Neal, Andrew D Trister, Tyler Cloke, Rita Sodt, Sunyoung Ahn, Anne L Baldock, Carly A Bridge, Albert Lai, Timothy F Cloughesy, Maciej M Mrugala, Jason K Rockhill, Russell C Rockne and Kristin R Swanson

PLOS ONE, 2013, vol. 8, issue 1, 1-7

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.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0051951

DOI: 10.1371/journal.pone.0051951

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