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AI and Breast Cancer: How Canadian Labs Use New Tech to Treat Patients – National
As artificial intelligence The Waterloo, Ontario Institute is taking breast cancer research to new heights by helping patients get the right treatment with new technologies.
When a woman has breast cancer, she usually has an imaging test, such as magnetic resonance imaging or an MRI, to look for cancerous tumors. The Waterloo Institute has created a “synthetic correlated diffusion” MRI that is tailored to capture cancer details and characteristics in a way not possible with his previous MRI system.
“It could be a very useful tool for oncologists and physicians to help identify and personalize the type of treatment cancer patients receive,” Waterloo told Global News.
According to the Canadian Cancer Society, breast cancer is the second leading cause of cancer death in Canadian women.
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It is estimated that 1 in 8 Canadian women will develop breast cancer in her lifetime and 1 in 34 will die.
Last year, an estimated 28,600 Canadian women were diagnosed with breast cancer. society said.
According to Wong, using synthetic correlated diffusion imaging data, a new AI-driven technique can predict whether neoadjuvant chemotherapy or chemotherapy given before surgery may benefit patients. increase.
The actual MRI machine hardware hasn’t changed in this model, but what has changed is how the technology sends “pulses” into the patient’s body and how the data is collected, Wong said. says.
“The cancer itself becomes brighter and actually shows different nuances and features around it. This makes it easier to identify not only the location and size of the cancer, but also the actual tissue characteristics of the cancer. Doctors make better decisions,” he said.
AI can then analyze the MRI data to help learn whether breast cancer patients can benefit from chemotherapy before surgery during the course of their treatment.
“We now have a wealth of information about tumor characteristics, so the AI in this case is a deep neural network, a bit like how our brain works. We learn to identify important nuances and traits that guide patients to benefit from modal chemotherapy,” said Wong.
“Basically, it’s a combination of two types of technology. One is the new MRI imaging technique to actually get the right information. The other is AI advances with deep neural networks.”
Deep neural networks can continue to improve as more information is acquired, Wong said.
“The more examples we see, the more accurately we can identify those subtle patterns that distinguish them from each other. As we train on more data, we can have higher levels of predictive accuracy.” he said.
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How accurate is the new technology?
The technology is being tested through a prospective study of about 253 patients in a U.S. cohort receiving chemotherapy before surgery, Wong said.
“Using a new form of MRI, AI was able to identify and predict which patients would benefit from chemotherapy with over 87% accuracy,” he said.
“Compared to the existing practice of clinicians looking at data and trying to predict what works and what doesn’t, I think this could be a very powerful tool. With such a tool, the physician can maximize the chances of choosing the right type of treatment, in this case chemotherapy, by targeting this particular patient based on his or her profile. It’s the one that’s most likely to help,” says Wong.
Given “promising results,” next steps include establishing a larger study in Canada, Wong said.
“best treatment”
Amy Tai, a graduate student in the Vision and Image Processing Lab at the University of Waterloo, started working on the technology after introducing the idea to the lab at the start of her course in May 2022.
“It was a whirlwind. It’s a bit like one of those dreamy moments.
“I was very excited to see the results, especially to see how accurate it is and how it could really help patients. We want to make sure you get the best possible care,” she said.
Tai explained that some types of treatment, such as chemotherapy, expose patients to radiation.
“If they are not expected to recover from it, or if there is a better treatment that is better suited to their tumor type or stage of breast cancer, ideally we would like them to have that treatment instead.
Now hoping to expand the patient cohort to more people, Tai said connecting with clinicians and learning more about how to successfully adopt the technology in clinical settings is also a priority. .
Responsible technology for the future
The AI tool was not created to replace doctors, Wong said, but serves as a complement to improve healthcare.
“In my opinion, AI is not meant to replace anyone, especially in this case, a doctor who has many years of experience treating patients,” he said.
“What we see is AI will always be there as a complementary tool or assistant doctor to help us make better decisions, more consistent decisions, and faster decisions.”
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According to Wong, physicians these days are getting used to AI and are incorporating it into their healthcare.
“Today, as doctors learn more about what AI can do and, more importantly, what it can’t do, they will become more comfortable with it and it is actually very welcome,” he said. said.
“There are actually many physicians who would like to work closely with us to see this kind of technology adopted into clinical care.”
AI continues to evolve and expand, Wong said, and the tool was created as part of building a responsible technology for the future.
“AI has become a very powerful tool and can be used for a variety of purposes, both malicious and benevolent. I am very happy.”
“One of the key things we’ve done is allow the AI, in particular, which we’re building, to be able to explain itself. And you can understand the rationale behind the predictions, which is even more exciting because that’s how you gain trust with your doctor.”
Wong, who has worked in the industry for more than a decade, is “excited by the potential” of the tool and its impact on healthcare in Canada.
“We are now at a point where we are seeing a sufficiently powerful combination with new medical imaging modalities that are really leading to very promising results that could greatly benefit healthcare in Canada,” he said. Told.
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