Johnston, Sandra K.

PurposeDespite the intra- and intertumoral heterogeneity seen in glioblastoma multiforme (GBM), there is little definitive data on the underlying cause of the differences in patient survivals. Serial imaging assessment of tumor growth allows quantification of tumor growth kinetics (TGK) measured in terms of changes in the velocity of radial expansion seen on imaging. Because a systematic study of this entire TGK phenotype—growth before treatment and during each treatment to recurrence —has never been coordinately studied in GBMs, we sought to identify whether patients cluster into discrete groups on the basis of their TGK.Patients and MethodsFrom our multi-institutional database, we identified 48 patients who underwent maximally safe resection followed by radiotherapy with imaging follow-up through the time of recurrence. The patients were then clustered into two groups through a k-means algorithm taking as input only the TGK before and during treatment.ResultsThere was a significant survival difference between the clusters ( P = .003). Paradoxically, patients among the long-lived cluster had significantly larger tumors at diagnosis ( P = .027) and faster growth before treatment ( P = .003) but demonstrated a better response to adjuvant chemotherapy ( P = .048). A predictive model was built to identify which cluster patients would likely fall into on the basis of information that would be available to clinicians immediately after radiotherapy (accuracy, 90.3%).ConclusionDichotomizing the heterogeneity of GBMs into two populations—one faster growing yet more responsive with increased survival and one slower growing yet less responsive with shorter survival—suggests that many patients who receive standard-of-care treatments may get better benefit from select alternative treatments.

Abstract With mean survival from glioblastoma multiforme (GBM) barely edging over 15 months, it become imperative to develop novel tools informing on both progression and treatment efficacy. As the aggressive growth of GBM outstrips available resources, regions of hypoxia develop. Hypoxia can be clinically assessed through [18F]-flouromisonidazole (FMISO) PET. However, PET imaging suffers from low resolution and reconstruction artifact. We present the comparative and predictive results of a multi-stage model utilizing the mathematical biology of cancer and the physics of PET imaging. Our mathematical model of GBM characterizes cancer cells into the interacting phenotypes of normoxic, hypoxic, and necrotic cells supported by vasculature. The pharmacokinetic activity of FMISO is induced in the simulated tissue then acquired and reconstructed by an analytic simulator of the PET process. This multi-stage process creates a patient-specific virtual PET images with characteristic of the clinical PET scan. Extending previous work, we model the full three-dimensional dynamics of tumor progression, pharmacokinetic activity, and FMISO PET for the tumor kinetics of six GBM patients. Virtual and clinical FMISO PET images are compared. Overall hypoxic burden and spatial distribution show strong correspondence between virtual and clinical FMISO PET images for all six patients. Simulated FMISO PET dynamics, with tumor kinetics derived from routine clinical MRI scans, provide a unique and evolving tool giving insight into the biological connections between magnetic resonance and molecular imaging. Citation Format: Joshua Jacobs, Andrea Hawkins-Darrud, Robert Harrison, Sandra Johnston, Paul Kinahan, Kristin Swanson. Correlating magnetic resonance and molecular imaging using three dimensional untreated virtual control. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A10.