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PLoS One. 2013;8(1):e51951. doi: 10.1371/journal.pone.0051951. Epub 2013 Jan 23.

Discriminating survival outcomes in patients with glioblastoma using a simulation-based, patient-specific response metric.

Author information

1
Department of Pathology, University of Washington, Seattle, Washington, United States of America. mneal@uw.edu

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.

PMID:
23372647
PMCID:
PMC3553125
DOI:
10.1371/journal.pone.0051951
[Indexed for MEDLINE]
Free PMC Article
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