Russell C. Langan, MD, FACS, FSSO, associate chief of Surgical Officer for System Integration and Quality and director of Surgical Oncology at Northern Region, RWJBarnabas Health and Rutgers Cancer Institute of New Jersey stated that many pancreatic cyst detection programs were “antiquated” and offered a new solution for early surveillance of pancreatic cysts.

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Langan, who spoke with CancerNetwork® during the 2024 Annual Oncology Clinical Practice and Research Summit, said that he, in partnership with Eon Health, helped build a computation linguistics model that streamlined and simplified the process of surveillance for potential patients with pancreatic cancer. The model recognizes patients at risk of developing pancreatic cancer, contacts them, and works to begin the intervention process.

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Langan was clear about the need for more technologically advanced means of pancreatic cyst surveillance. Prior to the model, patients were required to seek out these surveillance programs or be seen by a physician who had the requisite knowledge to refer them to a cyst program. The new model is intended to take much of the responsibility away from the patients.

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Transcript:

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In around 2017 or 2018, our institution, the Cooperman Barnabas Medical Center, and the health care system chose to start investing in preventative medicine. At that point in time, they partnered with a company that had an artificial intelligence software called a computational linguistics model that could identify incidental lung nodules, which are pre-cancerous, [and then] contact those patients and make sure that they receive the necessary surveillance so that intervention could potentially prevent lung cancer. I then approached that company and asked them about the pancreas because pancreatic cysts are similar to pulmonary nodules. They are the most common identifiable precursor to pancreatic cancer.

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These patients deserve lifelong surveillance when the cysts are mucinous and most of the surveillance programs, in my opinion, were antiquated. They, one, required patients to have some level of health literacy to find a pancreas cyst surveillance program, make an appointment, and get there, or they required a primary care [physician] or gastroenterologist to have knowledge about the risk of pancreatic cysts and refer them into a cyst program. Then the patient also had to follow up over time.

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On top of that, the individuals running the programs are running them off Excel spreadsheets. That is all just antiquated. I helped Eon [Health] build a computation linguistics model that’s specific to [the] pancreas to: one, improve the quality for a patient population that is living at risk for the development of pancreatic cancer, to ensure that they receive evidence-based guidelines surveillance, and receive the appropriate intervention to, at times, prevent the development of pancreatic cancer.

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In a separate video, Dr. Langan discussed how there are racial and ethnic minority disparities in the medical field's current ability to detect pancreatic cysts and pancreatic cancer. Langan said that the computational linguistics model he created with Eon helps mitigate the disparities by being agnostic to the medical record. This, he said, is beneficial because many patients who have lower socioeconomic status often use the emergency department (ED) as primary care treatment.

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For patients who receive imaging in the ED, the software will automatically identify risk factors like pancreatic cysts. The patient doesn’t have to follow-up and instead the software will contact the patient. Langan emphasized that this can lead a patient to quicker, higher-quality care than older tools would have led to. Beyond that, it expands the demographic of patients with access to treatment and surveillance.

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Transcript:

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One of the main limitations [with our methods of detecting pancreatic cysts and pancreatic cancer] is the fact that in traditional methods of pancreatic cyst surveillance or early cancer detection, there are racial and ethnic minority disparities, whereas our software runs in the medical record, it’s agnostic to a medical record, and it runs in the background. Patients who go to an ED, many times are of lower socioeconomic status, and many times use an ED as primary care treatment.

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If they are getting their imaging through the ED, our software will identify them. It can be the first point of contact to the patient [that] gets them expeditious, high-quality care. On top of improving quality for [detecting] cancer, it also improves the [ability] to mitigate these racial and ethnic disparities that exist in the natural delivery of health care.

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As seen on Cancer Network