Sunnybrook applies AI to breast-cancer radiation therapy

TORONTO – Researchers at the Sunnybrook Health Sciences Centre are using emerging artificial intelligence (AI) technologies to advance the diagnosis, treatments and outcomes of some of the world’s most debilitating diseases, including breast cancer. Dr. William Tran (pictured on right), radiotherapist and senior scientist in the Odette Cancer Program, has joined forces with Dr. Fang-I Lu (pictured on left), a breast pathologist at Sunnybrook and associate professor at the University of Toronto and together, they are developing AI technology to improve radiation therapy treatment for breast cancer patients.

The team is mapping the tumour immune microenvironment, the complex ecosystem of cells surrounding the breast’s tumour. Since the immune system plays a role in clearing tumour cells, the team aims to measure the probability of a tumour’s response to high-dose radiation treatment in women with high-risk breast cancer. The project involves taking thousands of tumour images and complex computational methods to recognize biomarkers associated with the tumour-killing effects of radiation treatment.

“Our work will allow doctors to determine which patients will benefit most from radiation therapy,” said Dr. Lu. “The ability to predict a tumour’s response to certain types of therapy has the potential to support more personalized and effective treatment plans for patients with advanced breast cancer.”

Breast cancer is one of the most common types of cancer affecting women in Canada. About one in eight women develop breast cancer in their lifetime. Starting in the cells of the breast, tumours can infiltrate nearby tissue or spread to other parts of the body. Breast cancer can be treated with surgery, drug therapy, and radiation therapy, but not all breast cancers are the same. Since every patient will respond to these treatments differently, predicting a tumour’s response to therapy can improve patient outcomes.

Radiation therapy is a common type of treatment for many cancers. The treatment uses high-energy beams to destroy the genetic material in cancerous cells that control how the cells grow and spread. Radiation therapy can also damage healthy cells in the body, which can cause short-term and long-term side effects like hair loss and fatigue.

“Personalizing the treatment plan using AI can help optimize treatment outcomes while minimizing the side effects,” said Dr. Tran, who is also an associate professor at the University of Toronto. “Our AI-based prediction model will help spare patients who are unlikely to benefit from radiation treatment from the short and long-term side effects associated with exposure to that type of therapy.”

Since 2018, the team has been researching the use of AI and digital pathology to map breast tissue samples and measure their resistance to neoadjuvant chemotherapy treatments. With this newest model currently undergoing planning for early-phase clinical trial testing, Dr. Tran hopes to continue improving personalized cancer treatment planning in the radiation oncology clinic.

Written by Anna McClellan