BackgroundThe heterogeneity of response to treatment in patients with glioblastoma multiforme suggests that the optimal therapeutic approach incorporates an individualized assessment of expected lesion progression. In this work, we develop a novel computational model for the proliferation and necrosis of glioblastoma multiforme.MethodsThe model parameters are selected based on the magnetic resonance imaging features of each tumor, and the proposed technique accounts for intrinsic cell division, tumor cell migration along white matter tracts, as well as central tumor necrosis. As a validation of this approach, tumor growth is simulated in the brain of a healthy adult volunteer using parameters derived from the imaging of a patient with glioblastoma multiforme. A mutual information metric is calculated between the simulated tumor profile and observed tumor.ResultsThe tumor progression profile generated by the proposed model is compared with those produced by existing models and with the actual observed tumor progression. Both qualitative and quantitative analyses show that the model introduced in this work replicates the observed progression of glioblastoma more accurately relative to prior techniques.ConclusionsThis image-driven model generates improved tumor progression profiles and may contribute to the development of more reliable prognostic estimates in patients with glioblastoma multiforme.
Standard therapy for glioblastoma (GBM) includes maximal surgical resection and radiation therapy. While it is established that radiation therapy provides the greatest survival benefit of standard treatment modalities, the impact of the extent of surgical resection (EOR) on patient outcome remains highly controversial. While some studies describe no correlation between EOR and patient survival even up to total resection, others propose either qualitative (partial versus subtotal versus complete resection) or quantitative EOR thresholds, below which there is no correlation with survival. This work uses a mathematical model in the form of a reaction–diffusion partial differential equation to simulate tumor growth and treatment with radiation therapy and surgical resection based on tumor‐specific rates of diffusion and proliferation. Simulation of 36 tumors across a wide spectrum of diffusion and proliferation rates suggests that while partial or subtotal resections generally do not provide a survival advantage, complete resection significantly improves patient outcomes. Furthermore, our model predicts a tumor‐specific quantitative threshold below which EOR has no effect on patient survival and demonstrates that this threshold increases with tumor aggressiveness, particularly with the rate of proliferation. Thus, this model may serve as an aid for determining both when surgical resection is indicated as well as the surgical margins necessary to provide clinically significant improvements in patient survival. In addition, by assigning relative benefits to radiation and surgical resection based on tumor invasiveness and proliferation, this model confirms that (with the exception of the least aggressive tumors) the survival benefit of radiation therapy exceeds that of surgical resection.
Summary Background Contrast-enhancing low-grade diffuse astrocytomas are an understudied, aggressive subtype at increased risk because of few radiographic indications of malignant transformation. In the current study, we tested whether tumor growth kinetics could identify tumors that undergo malignant transformation to higher grades. Methods Thirty patients with untreated diffuse astrocytomas (WHO II) that underwent tumor progression were enrolled. Contrast-enhancing and T2 hyperintense tumor regions were segmented and the radius of tumor at two time points leading to progression was estimated. Radial expansion rates were used to estimate proliferation and invasion rates using a biomathematical model. Results Radial expansion rates for both contrast-enhancing (p = 0.0040) and T2 hyperintense regions (p = 0.0016) were significantly higher in WHO II–IV tumors compared with nontransformers. Similarly, model estimates showed a significantly higher proliferation (p = 0.0324) and invasion rate (p = 0.0050) in WHO II–IV tumors compared with nontransformers. Conclusion Tumor growth kinetics can identify contrast-enhancing diffuse astrocytomas undergoing malignant transformation.
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