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AI can predict effectiveness of breast cancer chemo



Engineers in Canada have developed AI technology to predict if women with breast cancer would benefit from chemotherapy prior to surgery.

The new AI algorithm, part of the open-source Cancer-Net initiative led by Dr Alexander Wong, could help unsuitable patients avoid the serious side effects of chemotherapy and pave the way for better surgical outcomes for those who are suitable.

Wong, a professor of systems design engineering at the University of Waterloo, said:

“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.

“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.”

The AI software was trained with breast cancer images made with a new magnetic image resonance modality, invented by Wong and his team, called synthetic correlated diffusion imaging (CDI).

The project was led by Amy Tai, a graduate student with the Vision and Image Processing (VIP) Lab.

With knowledge obtained from CDI images of old breast cancer cases and information on their outcomes, the AI can predict if pre-operative chemotherapy treatment would benefit new patients based on their CDI images.

Known as neoadjuvant chemotherapy, the pre-surgical treatment can shrink tumours to make surgery possible or easier and reduce the need for mastectomies or other major surgery.

Wong said:

“I’m quite optimistic about this technology as deep-learning AI has the potential to see and discover patterns that relate to whether a patient will benefit from a given treatment.”

The new AI algorithm along with the complete dataset of CDI images of breast cancer have been made publicly available through the Cancer-Net initiative so other researchers can help advance the field.

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