Gliomas are invasive, fatal brain tumors that can be detected using magnetic resonance imaging (MRI). However, MRI does not reveal the entire size of tumor. By using a mathematical model developed by Kristin R. Swanson and colleagues, glioma growth can be quantified from the diffusion rate of the tumors cells, the net proliferation rate, and the carrying capacity of the tissue. These parameters are unique to each patient and can be determined by measuring the tumor volume from different MRI modalities as they change over time. Our lab collects data from patients consented to our study and records information about what treatments a given patient receives, since this can affect how the image appears. In my role as a member of the measurement team, I examine patient images to determine the extent of tumor burden on a given date. To obtain these measurements, I use Calcmri, a MATLAB program developed by our lab. Depending on the modality, enhancement on MRI slices can indicate tumor volume as well as other fluids. Calcmri can adjust the intensity threshold at which MRI enhancement will appear, which allows me to isolate the tumor and specify a region for the program to measure. I measure a series of two-dimensional cross section images in order to calculate a combined into 3-dimensional volume. These measurements are used to model the growth of the tumor, “in silico”. Our lab uses information from these models to discover clinical applications, such as to suggest more accurate treatment dosages and to predict life expectancy, which follows an overall goal to better capture the extent and invasion of tumor burden than what appears on MRI.

Glioblastoma Multiforme (GBM) is an aggressive type of primary brain tumor, which is treated by surgery followed by chemotherapy and radiation. A majority of cases prove to be highly resistant resulting in uniform fatality. At present, research has been moving towards developing a patient-centered model of treatment that is developed by finding the unique characteristics of a patient's tumor growth kinetics and response to therapy. GBM is a very heterogeneous disease despite having a generally poor prognosis. A promising method of investigating this heterogeneity has been through finding patterns in the molecular-genetic makeup of these tumors. Large-scale mathematical analysis on data from open-source repositories such as The Cancer Genome Atlas (TCGA) has already led to the discovery and validation of four GBM subclasses which point to characteristic areas of origination in the brain as well as differential growth behaviour. A limitation in this strictly data-oriented approach is the lack of meaningful clinical perspective applied which has resulted in the findings only being directed towards gauging patient survival. The focus of our research has been the discovery of information about individual characteristics of patients tumors that may have more clinical significance for each patient. Our hypothesis is that there is a correlation between the different biological subtypes classified by cluster analysis and the different kinetics of tumor growth measured by imaging. This work will be done by developing tools to investigate the genetic changes that may be driving different kinetics of disease progression (the proliferation and migration rates of the tumor cells) measured from actual patient imaging. This approach is guided by the belief that individual patient tumor biology can be used to inform the data analysis techniques and ultimately bring us closer to the goal of identifying the most discriminating treatment to effectively treat an individual's cancer.

The development of satellite technology has enabled scientists to develop a greater understanding of the movement patterns of various animals. Specifically, the movement of polar bears in Greenland is of ecological interest due to sea ice loss in the Arctic. Researchers tag polar bears with satellite collars and collect various samples and measurements such as age, sex, body mass, and body condition. Bears are tracked using polar-orbiting satellites and the data are collected and transmitted by Advanced Research and Global Observation Satellite (ARGOS). The large volumes of data are reported in coded text files that are difficult to analyze and contain error. I developed a program to extract data from ARGOS written using the statistical programming language R. This program allows for covariates on individual bears to be merged with satellite location data. I created two error checked databases (filtered and unfiltered) on movement of polar bears in Greenland. This program will facilitate data analysis of the movement patterns of polar bears.