Toronto-based Perimeter Medical Imaging wants to make breast conservation surgeries more efficient by combining an optical coherence tomography device with an AI engine to reduce the number of repeat surgeries required.
The possibility of repeat surgeries to remove cancer from women diagnosed with breast cancer is a reality of breast conservation surgery. In the U.S. about 25 percent of women need additional surgery after a lumpectomy, which is done to preserve breast tissue after an initial breast cancer diagnosis.
This is because, following a lumpectomy after which the patient is sent home, a pathologist’s report days or weeks later may find cancer at the margins of the excised breast tissue. And that positive margin means the patient has to undergo another surgery to fully remove cancer or undergo a full mastectomy. All of which is emotionally taxing for patients.
“They trust their surgeon that they are going to remove it and it’s a very difficult phone call that the surgeon has to make to the patient to say ‘I missed some of the cancer, so you have to come back for a surgery.'” said Andrew Berekely, co-founder of Perimeter Medical Imagine a Toronto startup that is developing an AI-powered solution to the problem of repeat breast cancer surgery. “That’s not a phone call that surgeons like to make.”
Perimeter Medical went public in a reverse takeover of a public company this week and is expected to start trading on the TSX Venture Exchange in Canada on July 6. The company raised private capital of C$10 million (about $7.3 million) from investors including Roadmap Capital, shareholders of the public company it took over and high net worth people. Perimeter Medical has also received a $7.4 million grant from the Cancer Prevention and Research Institute of Texas, according to Berkeley. The company has its U.S. offices in Dallas.
The technology that Perimeter hopes to commercialize includes a medical device that can perform optical coherence tomography (OCT) on excised breast tissue and then feed that imaging data into its AI engine to determine whether the margin is positive – in other words, does the tissue in the margins contain cancer. That real-time information can help to determine whether the surgeon needs to take out more of the breast tissue thereby perhaps reducing the chance of another surgery down the road.
“The OCT is used to scan the back of your eye which is a 1 cm area. We have adapted the technology to scan very large complex surfaces like a removed tissue specimen in a very fast amount of time and give information back in the operating room where it is a time-sensitive situation …,” Berkeley said.
That fast turnaround will be powered by the AI engine.
“We have custom-built machine learning algorithms specifically for OCT that are specifically trained on breast tissue and this is years worth of working and selecting clinical data. What we do is that we image the tissue and then we take the post-operative pathology – what the pathologist looks at under the microscope — and we do what’s called a correlation,” he explained. “So we take the exact same area [that a pathologist finds] when they see tissue under the microscope, [and] we can go back to the same area in the images and we label in our images what disease looks like and we feed that into the ML algorithm and the more we do that the better it gets at identifying new diseases.”
Originally published by
Arundhati Parmar | July 2, 2020