Pathology

Cancer emergence and progression are highly complex processes characterized by interactions among a large variety of cells and signalling molecules. It is very difficult to explain these complex interactions through linear thinking and molecular reductionist approaches. Mathematical modeling is a powerful tool that can substantially enhance our capacity for interpreting the data and generate new hypotheses.

This project aims to develop methods and software for performing spatio-temporal analysis of cancer incidence and smoking data in Ontario over long time periods with high spatial resolution. This will allow changes of cancer incidence over time to be better understood, and accommodate rare cancers that require long study periods in order to accumulate data. Because of small counts common to small area analysis, computationally intensive Bayesian inference methods will be needed.

Breast Microwave Radar is a promising new technology for breast cancer detection. Nevertheless, current image formation methods face issues that limit the use of this technology in clinical scenarios. The goal of this project is to use mathematical modeling and analysis to develop a novel image formation method for breast microwave radar suitable for use in realistic breast imaging settings. This technique will be capable of generating accurate and high contrast images for a specific patient in real time.

The objective of the project is to develop, evaluate and apply informative statistical methods to the task of identifying novel genes/pathways involved in breast cancer recurrence. A model for time to cancer recurrence using clinical, pathological, and molecular measures in the setting of high-dimensional genome-wide genetic scans will be developed that allows for a proportion of the patients to be long-term survivors.