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AI & Cancer Cell
detection

Faster cancer detection

AI technology joins cancer cell search

ECU’s Melanoma researchers have now teamed up with artificial intelligence specialists from September AI Labs to develop a way to use the technology to accurately identify cancer cells circulating in the blood.

Cancer spreads around the body when tumour cells shed from the primary tumour and travel through the blood to form secondary tumours (metastases) in other organs.

"By detecting and counting these circulating tumour cells (CTCs), clinicians and doctors can better understand what stage a cancer is at and predict the likelihood of a patient’s responsiveness to different treatments, thus improving patient outcomes," says ECU’s Associate Professor Elin Gray.

This AI technology has reduced this process down to a few minutes per patient.

The CTCs are incredibly difficult to spot among thousands of other cells and matter in blood; they are very rare, so it’s much like finding a needle in a haystack.

Within one millilitre of blood, there is often less than ten cancer cells amongst one billion red cells and one million white blood cells.

"Until now, it has taken a trained technician a few hours per patient sample to manually filter different characteristics of cells using traditional imaging techniques," according to Professor Gray.

Within one millilitre of blood, there is often less than ten cancer cells amongst one billion red cells and one million white blood cells.
Machine learning model trained to identify circulating tumour cells with a 97 per cent accuracy.

Pinpoint accurary

Using more than 4,000 images from the Melanoma Research Group at ECU, the September AI team developed a machine learning model that was trained to identify circulating tumour cells with a 97 per cent accuracy.

September AI Labs Managing Director Brad Dessington said the detection of CTCs was a particularly complex challenge for machine learning to achieve such high accuracy.

"CTCs are organic biological shapes and no one cell is the same. Each is different in size and shape and presents in random positions among healthy cells in the blood," Mr Dessington says.

"This was not just a matter of spotting the molecular signatures. The model had to be able to learn and understand complex images, to do it as well as a human, but far faster with robust neural networks and amped-up computer power."

ECU has entered into a partnership with September AI to ramp up artificial intelligence and machine learning accessibility for research across the University.

Through this partnership, the CTC identification technology will also be broadened to investigate a range of other cancers including lung, breast, pancreatic and prostate.

Building on a solid base

The AI techniques have built on world-leading research from the ECU Melanoma Research Group, who developed the world’s first blood test capable of detecting melanoma in its early stages.

The group are also collaborating with researchers from Harvard Medical School and clinicians at Western Australian hospitals to pioneer new techniques to detect circulating tumour cells that could provide a new avenue for cancer diagnosis and therapies.

Their team was the first group in the world to comprehensively describe the immense diversity found in melanoma CTCs.

That research published in the British Journal of Cancer this year was key to creating the dataset to train the AI algorithms built by the team from September AI.

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