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Chinese scientists achieve breakthrough in early detection of ‘king cancer’ that killed Steve Jobs

  • AI scientists and clinical researchers have worked together to develop an early screening method to detect pancreatic cancer
  • It could help save thousands of lives every year, with the difficulty in diagnosing pancreatic cancer making it one of the deadliest cancers

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A breakthrough method of early screening for pancreatic cancer, which combines medical imaging with an AI algorithm, could help save thousands of lives each year. Photo: Shutterstock
Dannie Peng
An artificial intelligence tool developed by Chinese scientists has led to a breakthrough in early-stage screening of one of the most fatal cancers.
Pancreatic cancer, often called the “king of cancers”, has an average five-year survival rate of less than 10 per cent. It killed Apple co-founder Steve Jobs in 2011, and more recently caused the death last month of Wu Zunyou, chief scientist at the Chinese Centre for Disease Control and Prevention.

Sumitomo Mitsui Financial Group – the globally renowned financial giant and Japan’s second-largest banking group – said on Monday that its CEO Jun Ohta died of pancreatic cancer on November 25, aged 65.

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One of the main reasons pancreatic cancer has such a high death rate is the difficulty in early detection. It is rarely found in its early stages, when the chance of curing it is at its greatest. That is because it often does not cause symptoms until it has spread to other organs, according to the Mayo Clinic.

But the early screening model – developed jointly by AI scientists from tech firm Alibaba Group’s DAMO Academy and clinical researchers from hospitals including the Shanghai Institution of Pancreatic Diseases – has shown promising results. Alibaba is the owner of the South China Morning Post.

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The model combines a non-contrast computed tomography (CAT) scan with an AI algorithm. In a paper published by the peer-reviewed journal Nature Medicine on Monday, the team said the specificity of the early screening model reached 99.9 per cent, implying there is only one false-positive case in every 1,000 tests.

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