In a study from Harvard University, scientists found artificial intelligence (AI) model trained with electronic health records could identify people with a much higher risk of developing pancreatic cancer within three to 36 months.
Pancreatic cancer is an aggressive cancer type that is often diagnosed at later stages due to its lack of early symptoms and therefore has a relatively poor prognosis.
Detecting pancreatic cancer earlier in the disease course may improve treatment options for these patients.
At the moment, there are no reliable biomarkers or screening tools that can detect pancreatic cancer early.
Recent advances in AI have led researchers to develop risk prediction algorithms for various types of cancer using radiology images, pathology slides, and electronic health records.
Models attempting to use precancer medical diagnoses—such as gastric ulcers, pancreatitis, and diabetes—as indicators of pancreatic cancer risk have had some success.
In the study, researchers aimed to develop an artificial intelligence tool that can help clinicians identify people at high risk for pancreatic cancer so they can be enrolled in prevention or surveillance programs and hopefully benefit from early treatment.
The team sought to develop more accurate models by incorporating concepts from language processing algorithms.
They trained their AI method using electronic health records from the Danish National Patient Registry, which included records from 6.1 million patients treated between 1977 and 2018, around 24,000 of whom developed pancreatic cancer.
The researchers inputted the sequence of medical diagnoses from each patient to teach the model which diagnosis patterns were most significantly predictive of pancreatic cancer risk.
They then tested the ability of the AI tool to predict the occurrence of pancreatic cancer within intervals ranging from three to 60 months after risk assessment.
At a threshold set to minimize false positives, individuals considered “at high risk” were 25 times more likely to develop pancreatic cancer from three to 36 months than patients below the risk threshold.
In contrast, a model that did not take the sequence of precancer disease events into account resulted in a substantially lower increased risk for patients above a corresponding threshold.
The researchers further validated their findings using electronic medical records from the Mass General Brigham Health Care System.
These results indicate the potential of advanced computational technologies, such as AI and deep learning, to make increasingly accurate predictions based on each person’s health and disease history.
For more information about pancreatic cancer, please see recent studies about herbs that may help treat pancreatic cancer, and results showing common opioid painkillers may increase pancreatic cancer risk.
The research was presented at the AACR Annual Meeting 2022 and was conducted by Bo Yuan et al.
Copyright © 2022 Knowridge Science Report. All rights reserved.