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AI Predicts Breast Cancer Chemotherapy Effectiveness

Scientists at the University of Waterloo have developed a new artificial intelligence tool, Cancer-Net, to predict the benefits of chemotherapy for breast cancer patients prior to surgery, improving recovery and survival outcomes.

Scientists at the University of Waterloo have created a new artificial intelligence (AI) tool called Cancer-Net that can predict if women with breast cancer will benefit from chemotherapy prior to their surgery. This new technology could help avoid the serious side effects of chemotherapy for women who wouldn’t benefit from it, and ensure better surgical outcomes for those who would.

Determining the right treatment for a patient with breast cancer is a challenging task for doctors. The AI system created by Dr. Alexander Wong and his team gives doctors the ability to prescribe the best personalized treatment for a patient, improving their recovery and survival chances. The AI software was trained using images of breast cancer obtained using a new method called synthetic correlated diffusion imaging (CDI).

The AI was trained using CDI images of old breast cancer cases, along with information on their outcomes. With this information, the AI can predict if pre-operative chemotherapy would benefit new patients based on their CDI images. This type of chemotherapy, known as neoadjuvant chemotherapy, is given before surgery and can shrink tumors, making the surgery easier or even avoiding the need for major surgery such as a mastectomy.

Dr. Wong is optimistic about the potential of deep-learning AI to discover patterns that relate to whether a patient will benefit from a given treatment. The Cancer-Net initiative has made the complete dataset of CDI images and the AI algorithm publicly available, so that other researchers can help advance the field.

A recent study on the Cancer-Net BCa project was presented at the Med-NeurIPS conference, a major international conference on AI. The study discussed the use of volumetric deep radiomic features from synthetic correlated diffusion imaging for predicting the pathologic complete response in breast cancer patients.

In conclusion, the Cancer-Net initiative is a promising development in the field of AI and breast cancer treatment. The ability to predict the effectiveness of chemotherapy prior to surgery can have a significant impact on a patient’s treatment plan and overall outcome. The availability of the complete dataset and AI algorithm for other researchers to use and build upon will help drive further advancements in this field.