Engineers at the University of Waterloo have developed an artificial intelligence (AI) tool that can help cancer specialists determine whether patients with breast cancer should receive chemotherapy prior to surgery.
The new algorithm could help some patients avoid the serious side-effects of chemotherapy, while making it easier for patients who would benefit from the treatment to receive it. It's part of the open-source Cancer-Net information-sharing initiative led by Dr. Alexander Wong, a professor of systems design engineering at the university.
"Determining the right treatment for a given breast cancer patient is very difficult right now, and it is crucial to avoid unnecessary side effects from using treatments that are unlikely to have real benefit for that patient," Wong said in media release issued on Tuesday.
"An AI system that can help predict if a patient is likely to respond well to a given treatment gives doctors the tool needed to prescribe the best personalized treatment for a patient to improve recovery and survival."
BARRIERS TO TREATMENT
Breast cancer is the second most common type of cancer in women in Canada and the United States, representing over 25 per cent of all new female cancer cases.
Cancer specialists often turn to surgery to remove cancerous tissue and halt the growth of breast cancer, but some non-metastatic breast cancer tumours are inoperable.
Increasingly, specialists are turning to a treatment called neoadjuvant chemotherapy to solve this problem. The treatment works by shrinking large tumours to make surgery possible, or at least easier. It can also reduce the need for major surgery such as mastectomies.
However, not everyone is an ideal candidate for neoadjuvant chemotherapy.
"Neoadjuvant chemotherapy is expensive, time-consuming, and may expose patients to radiation as well as lead to other significant side-effects such as reduced fertility," the authors, led by graduate student Amy Tai, wrote in a paper published in November 2022.
Tai, Wong and their co-authors set out to create an algorithmic tool that would help doctors figure out which patients would benefit from the treatment and which would not. The team trained the AI algorithm using images of breast cancer made with a new MRI method they developed called synthetic correlated diffusion imaging (CDI).
Armed with knowledge gleaned from CDI images of old breast cancer cases and information on their outcomes, the AI tool has learned to accurately predict if patients would benefit from pre-surgical chemotherapy based on their CDI images.
The next step for the team is to validate their data by testing the technology in a comprehensive study involving a larger group of patients.
"I'm quite optimistic about this technology," Wong said, "as deep-learning AI has the potential to see and discover patterns that relate to whether a patient will benefit from a given treatment."
