Research News
Computer Model Integrates Imaging and Histology to Better Predict Brain Cancer Outcome
Brain tumors known as gliomas are almost always lethal, though the time from diagnosis to death can vary greatly. Patients with low-grade gliomas can sometimes live for decades, while the lifespan for patients with high-grade, aggressive gliomas can be measured in months. Now, using computational techniques that account for a number of key interactions between a glioma and its microenvironment, a research team at the H. Lee Moffitt Cancer Center Physical Sciences-Oncology Center, has developed a computer model that may be able to predict the dynamic changes that occur in gliomas.
Kristin Swanson from the University of Washington and Alexander Anderson led the research team that conducted this study. They published their findings in the journal Cancer Research.
Starting with a simple but clinically useful “proliferation-invasion” model that Swanson, Anderson, and colleagues have been developing for 15 years, the researchers incorporated details about the distinct cellular and microenvironmental changes related to angiogenesis that occur in gliomas. In particular, this more sophisticated model accounts for the fact that a given glioma contains three different environments represented by normal oxygen levels, low oxygen levels, and dying or necrotic cells. The model also accounts for regions in which new blood vessels are growing and for levels of diffusible angiogenic factors (various growth factors that trigger new blood vessel growth). The researchers call this a “proliferation–invasion–hypoxia–necrosis–angiogenesis” (PIHNA) model.
Running the model using a wide range of parameters, the investigators found that the PIHNA model duplicates the key characteristics of all grades of invasive gliomas and produces estimates of increasing cell number, vascularization, hypoxia, and necrosis that are consistent with the grading scheme currently used by pathologists to characterize gliomas. These simulations also reproduced the wave of hypoxia at the leading edge of the tumors that were observed in recent magnetic resonance imaging experiments.
To demonstrate the clinical potential for this model, the investigators used patient-specific histological and imaging data to accurately predict hypoxia, necrosis, and histological grade. Further work demonstrated that both cell proliferation and invasiveness are critical to correctly predicting the clinical path of a particular glioma, and tumors that appear to be the same grade histologically will develop along different time courses depending on their relative values for proliferation and invasiveness. This last finding is consistent with the observation that patients with similar histological grade tumors do not all follow the same time course to progression and death.
This work, which is detailed in a paper titled, "Quantifying the Role of Angiogenesis in Malignant Progression of Gliomas: In Silico Modeling Integrates Imaging and Histology," was supported in part by the National Cancer Institute's Physical Sciences in Oncology initiative, a program that aims to foster the development of innovative ideas and new fields of study based on knowledge of the biological and physical laws and principles that define both normal and tumor systems. An abstract of this paper is available at the journal's Web site.
View abstract
