Pancreatic cancer is one of the deadliest cancers in the world, and its toll is projected to increase. The findings, published in the journal Nature Medicine, suggest that AI-based population screening could be valuable in finding those at elevated risk for the disease and could expedite the diagnosis of a condition found all too often at advanced stages when treatment is less effective and outcomes are dismal.
Currently, there are no population-based tools to screen broadly for pancreatic cancer. Those with a family history and certain genetic mutations that predispose them to pancreatic cancer are screened in a targeted fashion. But such targeted screenings can miss other cases that fall outside of those categories, the researchers said.
An AI tool that identifies those at the highest risk for pancreatic cancer would ensure that clinicians test the right population, while sparing others unnecessary testing and additional procedures, they said.
“One of the most important decisions clinicians face day to day is who is at high risk for a disease, and who would benefit from further testing, which can also mean more invasive and more expensive procedures that carry their own risks,” said Chris Sander, faculty member in the Department of Systems Biology in the Blavatnik Institute at Harvard Medical School.
“An AI tool that can zero in on those at highest risk for pancreatic cancer who stand to benefit most from further tests could go a long way toward improving clinical decision-making,” Sander added.
Applied at scale, Sander added, such an approach could expedite detection of pancreatic cancer, lead to earlier treatment, and improve outcomes and prolong patients` life spans.
About 44 per cent of people diagnosed in the early stages of pancreatic cancer survive five years after diagnosis, but only 12 per cent of cases are diagnosed that early. The survival rate drops to 2 to 9 per cent in those whose tumours have grown beyond their site of origin, they estimate.
In the new study, the AI algorithm was trained on two separate data sets totalling 9 million patient records from Denmark and the US. The researchers `asked` the AI model to look for telltale signs based on the data contained in the records.
Based on combinations of disease codes and the timing of their occurrence, the model was able to predict which patients are likely to develop pancreatic cancer in the future. Notably, many of the symptoms and disease codes were not directly related to or stemming from the pancreas.
The researchers tested different versions of the AI models for their ability to detect people at elevated risk for disease development within different time scales — 6 months, one year, two years, and three years.
Overall, each version of the AI algorithm was substantially more accurate at predicting who would develop pancreatic cancer than current population-wide estimates of disease incidence — defined as how often a condition develops in a population over a specific period of time.
The researchers said they believe the model is at least as accurate in predicting disease occurrence as are current genetic sequencing tests that are usually available only for a small subset of patients in data sets.