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July 19, 2019updated 23 Dec 2019 10:22am

AI-based CompCyst test shows promise in pancreatic cancer study

A proof-of-concept study led by Johns Hopkins Kimmel Cancer Center in the US has found CompCyst, an artificial intelligence (AI)-based laboratory test, could accurately identify pancreatic cysts that will develop into cancers.

A proof-of-concept study led by Johns Hopkins Kimmel Cancer Center in the US has found CompCyst, an artificial intelligence (AI)-based laboratory test, could accurately identify pancreatic cysts that will develop into cancers.

CompCyst builds on patient data including clinical impressions and symptoms, CT images, and molecular markers such as DNA alterations in cyst fluid.

During the study, the research team used data from more than 800 patients with pancreatic cysts.

It was observed that the AI-based test correctly differentiated more patients who may or may not benefit from surgery or require further monitoring than existing standard clinical and imaging approaches.

The researchers added that the new test would have avoided cyst removal in more than 50% of patients, where the surgery was later considered unnecessary as the cysts were unlikely to have developed into cancer.

Johns Hopkins multidisciplinary pancreatic cyst clinic director Anne Marie Lennon said: “Our study demonstrates the potential role of CompCyst as a complement to existing clinical and imaging criteria when evaluating pancreatic cysts.

“It could provide a greater degree of confidence for physicians when they advise patients that they do not require follow-up and can be discharged from surveillance.”

The team performed histopathological analysis of resected surgical specimens to determine the exact nature of the cysts.

They assessed molecular profiles of 862 pancreatic cysts and fed this information as well as clinical and radiologic data into a computer programme that used AI to classify patients based on the nature of their cysts.

The researchers noted that the study has several limitations, including extraction of cyst fluid at the time of surgery and assessment of cysts that are more atypical than those found in clinical practice.

They intend to further assess the AI-based test in a prospective validation study next year.

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