AI can predict pancreatic cancer in some cases, study shows

Pancreatic cancer is one Low Five-year survival rates for any cancer are poor, in part because late detection is common. Can artificial intelligence change that?

Research AI screening of large cohorts of patients could make earlier diagnosis possible, leading to earlier and more effective treatment of the disease, published in Nature Medicine in May. In the analysis, an AI tool successfully pinpointed people at high risk for pancreatic cancer by looking back at their medical records, finding evidence of elevated risk up to three years before they were diagnosed.

The researchers used data from the medical records of patients in both the United States and Denmark from 1977 to 2020. They looked at a cohort of 6.2 million Danish patients, of whom 23,985 had pancreatic cancer, and 3 million military personnel were being treated. Through Veterans Affairs, 3,864 of them were eventually identified.

The researchers used a machine learning model to analyze the data, teaching it to predict cancer risk based on symptoms and various diagnostic codes in patients’ medical records.

Some of the symptoms associated with a high-risk prognosis are not traditionally linked to pancreatic cancer. Gallstones, type 2 diabetes, anemia and gastrointestinal symptoms such as vomiting and abdominal pain were all linked to a higher risk score three years before diagnosis.

In a real-world scenario, the researchers write, the AI ​​model would develop pancreatic cancer in 320 of every 1,000 people identified as being at high risk. By targeting surveillance to high-risk patients, the tool could make screening more affordable, they write.

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Currently, the US Preventive Services Task Force does not recommendation Screening asymptomatic people for pancreatic cancer. Screening of high-risk patients related to However, there is a higher chance of long-term survival.

“An AI tool that can zero in on people who are at high risk for pancreatic cancer, and who would benefit most from trials, could go a long way toward improving clinical decision-making,” said study co-author Chris Sander, a biologist. Harvard Medical School Laboratory In one message, biology is dedicated to using machine learning and other technologies to solve problems liberation.

Used at scale, such a tool could extend lifespan and improve treatment outcomes, Sander said.

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