Farinaz Forouzannia, Applied Mathematics, University of Waterloo
The cancer risk associated with high and low doses of radiation
Radiotherapy uses high doses of energy to eradicate cancer cells and thus arrest the growth of cancerous tumors. Radiation oncologists try to deliver the maximum amount of radiation to cancerous cells (while simultaneously minimizing energy delivered to adjacent normal tissues) in order to maximize cancer cell kill while preventing damage to the normal cells around the affected tissue. To achieve this goal, various treatment schedules have been developed, but there still remain significant obstacles to improving the effectiveness of these schedules. Hence the development of better-designed treatment protocols is still a field of significant research activity. In addition, radiation therapy can cause both short-term and long-term side effects, which should be determined along with the quality of the treatment. For example, second cancer is one of the major concerns in long-term radiation therapy survivors. The effects of high doses of radiation and the possible harmful clinical consequences have been studied extensively; in contrast much less research effort has been focused on the effects of lower doses of radiation. It is clear that with the pervasiveness of new technologies (and associated emission of low radiation), the risks associated with lower doses of radiation merits further study.
In this research, stochastic and deterministic analyses will be used to predict and design a better treatment schedule for breast cancer. The proposed model will also be improved and used to investigate the effects of repair mechanisms on cancer cells during and after radiotherapy using different treatment schedules and the newly designed protocol. A proper mathematical model will also be developed in order to study the second cancer risks associated with radiotherapy. Finally, the newly developed model will be used to analyze the risks attributed with lower doses of radiation based on the underlying biological features.