Dr. Phedias Diamandis
Dr. Phedias Diamandis and his team at Princess Margaret Cancer Centre have created an artificial-intelligence-driven tool they call "HAVOC" to help researchers better understand tumour heterogeneity. "It's a useful tool to complement other molecular approaches and contribute to ongoing personalized medicine efforts," he says. (Photo: UHN Research Communications)​

By Mimi Yuejun Guo

"It is a scary topic when someone tells you that your job is going to be replaced by AI (artificial intelligence)," says Dr. Phedias Diamandis, a neuropathologist at UHN.

Modern AI's image analysis mirrors a pathologist's clinical skills in examining microscopic tissue images to diagnose diseases. Concerns about AI overtaking pathology have existed for years.

To transform the challenge into opportunities, Dr. Diamandis stepped foot in the world of AI in 2017, when the concerns began to spread.

"I wanted to see if AI can interpret pathology images like humans are trained to do," he says.

Drawing upon his neuroscience background and extensive self-learning, Dr. Diamandis, a scientist at the cancer centre, a neuropathologist at UHN and an associate professor in the Department of Laboratory Medicine and Pathobiology at the University of Toronto, discovered that AI learns in a manner comparable to humans.

"Our perception relies on neural networks in our brain," he says. "In the visual system, primary visual centres detect basic shapes like circles and squares, while higher-level centres integrate them into complex objects.

"Similarly, AI analyzes images by identifying basic shapes through spatial coordinates and checking for spatial distribution to recognize familiar patterns."

With an understanding of how AI works and a goal to apply AI in pathology, Dr. Diamandis' team started a research project to train AI to recognize tumour histology slides.

The team fed AI nearly one million images collected from more than 1,000 brain tumours, each annotated by pathologists. Using deep neural networks, they developed a tool to analyze cell patterns and generate a map highlighting distinct regions of the tumour with their unique histomorphological features.

They call this AI-driven tool "HAVOC" (Histomic Atlases of Variation Of Cancers). HAVOC aims to help researchers better understand tumour heterogeneity – a phenomenon where different regions of a tumour can have different biology, which can lead to different treatment responses and resistance.

Understanding cancer variations helps guide personalized treatment and improve precision medicine.

But how well does it work?

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