National Taiwan University (NTU) Hospital has developed an artificial intelligence (AI) algorithm to improve radiologists’ ability to detect pancreatic cancer during computed tomography (CT) scans.
Pancreatic cancer killed 2,497 people in Taiwan last year and it is the seventh leading cause of death in the nation, Liao Wei-chih (廖偉智), an attending physician at the hospital’s Division of Gastroenterology, said on Tuesday last week at a conference held by the National Health Insurance Administration.
The conference discussed innovative medical applications of artificial intelligence using data provided by the agency, including more than 64.9 billion pieces of structured data on medical records of Taiwanese and 2.3 billion pieces of unstructured image data.
Photo: Lin Hui-chin, Taipei Times
While CT scans are the main tool used to detect pancreatic cancer, about 40 percent of tumors smaller than 2cm evade detection, Liao said.
During the early stage, pancreatic cancer shows almost no signs or symptoms, and the tumor often has indistinct borders, making it difficult to identify even by professional radiologists, he said.
Liao cited a case in which a CT scan showed no signs of pancreatic cancer, but an endoscopic ultrasonography identified a 1.5cm tumor in the patient.
Not every medical institution provides endoscopic ultrasonography services, so patients there could miss a timely diagnosis, he added.
The algorithm, trained by 3,000 pieces of image data, can yield a 98.3 percent sensitivity rate in distinguishing image patches of pancreatic cancer from non-cancerous ones, higher than the 92.9 percent of radiologists, he said.
It can detect cancerous tissue as small as 1cm, he added.
The five-year survival rate of pancreatic cancer patients is only 7.7 percent, because most of them have a late diagnosis, Liao said, adding that the rate could increase to 80 percent if tumors can be identified and removed before reaching 2cm, he said.
Although the algorithm does not to completely replace radiologists’ role in reading CT images, it can shorten their time of interpretation and reduce errors, he added.
Research results on the algorithm were published by the Lancet Digital Health in July.
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