University Of Waterloo Scientists Utilize AI To Predict Efficacy Of Breast Cancer Chemotherapy In Individual Patients.

Researchers from the University of Waterloo-Canada are using AI or artificial intelligence to predict the efficacy of breast cancer chemotherapy as per each patient case.


 
At present, breast cancer is the second most common type of cancer in women in Canada and the United States, representing over 25% of all new female cancer cases.
 
Neoadjuvant chemotherapy treatment has recently risen in usage as it may result in a patient having a pathologic complete response (pCR), and it can shrink inoperable breast cancer tumors prior to surgery so that the tumor becomes operable, but it is difficult to predict a patient's pathologic response to neoadjuvant chemotherapy.
 
The study team investigated the efficacy of leveraging learnt volumetric deep features from a newly introduced magnetic resonance imaging (MRI) modality called synthetic correlated diffusion imaging (CDIs) for the purpose of pCR prediction.
 
The study team more specifically, leveraged a volumetric convolutional neural network to learn volumetric deep radiomic features from a pre-treatment cohort and construct a predictor based on the learnt features using the post-treatment response.
 
This is the first study to date to explore the utility of CDIs within a deep learning perspective for clinical decision support.
 
The study team evaluated the proposed approach utilizing the ACRIN-6698 study against those learnt using gold-standard imaging modalities, and found that the proposed approach can provide enhanced pCR prediction performance and thus may be a useful tool to aid oncologists in improving recommendation of treatment of patients.
 
This approach can subsequently be used to leverage volumetric deep radiomic features (which the study team named as Cancer-Net BCa) and can be further extended to other applications of CDIs in the cancer domain to further improve prediction performance.
 
The study findings were published on a preprint server: Arxiv
https://arxiv.org/abs/2211.05308
 
The Canadian engineers from the University of Waterloo are the first have developed such an artificial intelligence (AI) technology to predict if women with breast cancer would benefit from chemotherapy prior to surgery.
 
The novel AI algorithm, part of the open-source Cancer-Net initiative led by Dr Alexander Wong, a professor of systems design engineering at the University of Waterloo, could help unsuitable candidates avoid the serious side effects of chemotherapy and pave the way for better surgical outcomes for those who are suitable.
 
Dr Wong told Breast Cancer News, "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.”
 
He further added, "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 project was also led by Amy Tai, a graduate student with the Vision and Image Processing (VIP) Lab and the AI software was trained with images of breast cancer made with a new magnetic image resonance modality, invented by Dr Wong and his study team, 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 can predict if pre-operative chemotherapy treatment would benefit new patients based on their CDI images.
 
Commonly referred to as neoadjuvant chemotherapy, this pre-surgical treatment can shrink tumors to make surgery possible or easier and reduce the need for major surgery such as mastectomies.
 
Dr Wong, who is also a director of the VIP Lab and the Canada Research Chair in Artificial Intelligence and Medical Imaging added, "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.”
 
A study report on the project, Cancer-Net BCa: Breast Cancer Pathologic Complete Response Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging, was recently presented at Med-NeurIPS as part of NeurIPS 2022, a major international conference on AI.
 
This novel AI algorithm and 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.
 
It is hoped that this new AI strategy can save time for oncologists and physicians to choose the most optimal treatment for their patients and also help to save lives.
 
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