Much faster identification of the cells that spread cancer throughout the body has been made possible by ECU researchers using advanced artificial intelligence (AI) technology.
ECU’s Melanoma Research Group has teamed up with AI specialists from September AI Labs to fast-track the accurate identification of cancer cells circulating in the blood.
Together they have designed a process that accurately identifies dangerous cells within a few minutes rather than several hours.
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, therefore significantly improving patient outcomes,” ECU’s Associate Professor Elin Gray said.
Professor Gray’s team have pioneered a multimarker approach to detecting CTCs in collaboration with Harvard Medical School and clinicians at Western Australian hospitals.
“The CTCs are incredibly difficult to spot among thousands of other cells and matter in blood -- they are very rare much like finding a needle in a haystack,” Professor Gray said.
“Within one millilitre of blood, there are often less than ten cancer cells among one billion red cells and one million white blood cells.
“Until now, it has taken a trained technician several hours per patient sample to manually filter different characteristics of cells using traditional imaging techniques.
“This AI technology has reduced this process down to a few minutes per patient.”
Using more than 4000 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 said.
“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.
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