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. 2009 Dec 1;69(23):9133-40.
doi: 10.1158/0008-5472.CAN-08-3863. Epub 2009 Nov 24.

Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model

Christina H Wang  1 , Jason K Rockhill, Maciej Mrugala, Danielle L Peacock, Albert Lai, Katy Jusenius, Joanna M Wardlaw, Timothy Cloughesy, Alexander M Spence, Russ Rockne, Ellsworth C Alvord Jr, Kristin R Swanson
Affiliations

Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model

Christina H Wang et al. Cancer Res. .

Abstract

Glioblastomas are the most aggressive primary brain tumors, characterized by their rapid proliferation and diffuse infiltration of the brain tissue. Survival patterns in patients with glioblastoma have been associated with a number of clinicopathologic factors including age and neurologic status, yet a significant quantitative link to in vivo growth kinetics of each glioma has remained elusive. Exploiting a recently developed tool for quantifying glioma net proliferation and invasion rates in individual patients using routinely available magnetic resonance images (MRI), we propose to link these patient-specific kinetic rates of biological aggressiveness to prognostic significance. Using our biologically based mathematical model for glioma growth and invasion, examination of serial pretreatment MRIs of 32 glioblastoma patients allowed quantification of these rates for each patient's tumor. Survival analyses revealed that even when controlling for standard clinical parameters (e.g., age and Karnofsky performance status), these model-defined parameters quantifying biological aggressiveness (net proliferation and invasion rates) were significantly associated with prognosis. One hypothesis generated was that the ratio of the actual survival time after whatever therapies were used to the duration of survival predicted (by the model) without any therapy would provide a therapeutic response index (TRI) of the overall effectiveness of the therapies. The TRI may provide important information, not otherwise available, about the effectiveness of the treatments in individual patients. To our knowledge, this is the first report indicating that dynamic insight from routinely obtained pretreatment imaging may be quantitatively useful in characterizing the survival of individual patients with glioblastoma. Such a hybrid tool bridging mathematical modeling and clinical imaging may allow for stratifying patients for clinical studies relative to their pretreatment biological aggressiveness.

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Figures

Figure 1
Figure 1
a) “Tip of the iceberg” analogy showing a threshold of detection based on concentrations of tumor cells (as well as leaky blood vessels) contributing to a gradient of glioma cells extending well beyond the T1-Gd MRI-defined threshold of detection and even beyond the T2-defined threshold. Our biologically-based mathematical model quantifies this growth and invasion of the glioma cells contributing to this overall profile by rates of net proliferation (ρ) driving the concentration of cells up and net dispersal (D) driving the concentration of cells peripherally. b) Model-predicted diffuse T2 gradient of glioma cells predicted by simulation of the bio-mathematical model on an anatomically-accurate brain phantom (3, 4) using model parameters (D and ρ) specific to Patient 11 in Table 2. The T1Gd MRI-detectable edge of the lesion is superimposed as a dark grey contour.
Figure 2
Figure 2
a) The ratio of the actual survival to the RPA-predicted median survival vs. the velocity of radial growth seen on T1Gd MRI and b) vs the net proliferation rate ρ. c) The Therapeutic Response Index (TRI), the ratio of the actual survival to the model-predicted untreated survival, vs the velocity of radial growth seen on T1Gd MRI and d) vs the net proliferation rate ρ.
Figure 3
Figure 3
a) Actual survival time vs predicted survival time of the untreated virtual control (UVC) showing the line of identity corresponding to a Therapeutic Response Index of 1.0. b) Sensitivity and specificity of standard clinical parameters and patient-specific model parameters in predicting the patients who will respond to treatments and survive longer than 150% of their baseline (untreated) model-predicted survival (TRI > 1.5).
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