Grant: DRA : Canada Graduate Research Scholarship - Doctoral Program (CGRS D)

Breast conserving surgery is the most common treatment for women with early-stage breast cancer. However, up to 30% of patients need a second surgery because the initial operation did not remove enough cancer. This can delay further treatment, increase complications, and worsen quality of life. One reason this happens is that breast cancers are not all the same. Some grow slowly, while others are more aggressive and tend to spread into nearby tissue. Surgeons use this subtype information try to leave a 'safe border' of healthy tissue around the tumour to make sure no cancer cells are left behind. However, if the tumour is more aggressive than expected, this border may not be wide enough, leading to a need for repeat surgery. Current biopsies can miss the more aggressive parts of a tumour up to 40% of the time, resulting in the 'safe border' not being removed. A new tool, called the "iKnife," analyzes smoke produced during surgery to identify tissue types in real time. It has already been shown to distinguish cancer from healthy tissue, and even detect certain genetic changes in breast tumours. If combined with other information like mammograms, ultrasounds, and patient characteristics, the iKnife could help surgeons identify the subtype of breast cancer during surgery itself, guiding what the 'safe border' will be in real time. My project will test this approach in three steps. First, we will collect and compare iKnife data with established breast cancer subtypes to see how they can be predicted. Second, we will combine iKnife results with imaging and clinical data to improve prediction accuracy. Finally, we will explore how this combined tool could be used during surgery to guide decisions without disrupting normal surgeon workflow. If successful, this method could reduce the number of women needing repeat surgery, speed up access to the proper treatments, improve patient outcomes, and improve healthcare efficiency.