An AI Model may Predict Elevated Pancreatic Cancer Risk Using EHR

An artificial intelligence (AI) model trained using sequential health information derived from electronic health records (EHR) identified a subset of individuals with a 25-fold risk of developing pancreatic cancer within three to 36 months, according to results presented at the AACR Annual Meeting 2022, held April 8-13.

"At the moment, there are no reliable biomarkers or screening tools that can detect pancreatic cancer early," said Bo Yuan, a PhD candidate at Harvard University, who presented the study. "The purpose of this study was 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."

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, said Davide Placido, a PhD candidate at University of Copenhagen and co-first author of the study. Detecting pancreatic cancer earlier in the disease course may improve treatment options for these patients, he noted.

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, but Yuan and colleagues sought to develop more accurate models by incorporating concepts from language processing algorithms.

"We were inspired by the similarity between disease trajectories and the sequence of words in natural language," Yuan said. "Previously used models did not make use of the sequence of disease diagnoses in an individual’s medical records. If you consider each diagnosis a word, then previous models treated the diagnoses like a bag of words rather than a sequence of words that forms a complete sentence."

The researchers 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.

The researchers 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. The differences in health care and recordkeeping practices between different health care systems required the model to be retrained on the new dataset, Yuan said, and upon retraining, the model performed with comparable accuracy; the area under the curve (a measurement of accuracy that increases as the value approaches 1) for this dataset was 0.88 as compared with 0.87 for the original training set.

Although most of the AI’s decision making happened in the "hidden layers" of a complex neural network, making it difficult for the researchers to pinpoint exactly what diagnosis patterns predicted risk, Yuan and colleagues found significant associations with certain clinical characteristics and pancreatic cancer development. For example, diagnoses of diabetes, pancreatic and biliary tract diseases, gastric ulcers, and others were associated with increased risk of pancreatic cancer. While this knowledge may improve traditional risk stratification in some cases, the advantage of the AI tool is that it integrates information about risk factors in the context of a patient’s disease history, Placido said.

"The AI system relies on these features in context, not in isolation," Yuan said.

The researchers - including co-first author Jessica Hjaltelin, PhD; co-senior authors Søren Brunak, PhD, and Chris Sander, PhD; and collaborators Peter Kraft, PhD, Michael Rosenthal, MD, PhD, and Brian Wolpin, MD, MPH - hope this research, once evaluated in clinical trials, will lead to identifying patients with an elevated pancreatic cancer risk. This could potentially help recruit high-risk patients into programs centered around prevention and increased screening for early detection. If the cancer is caught early, Placido said, the odds of successful treatment are higher.

"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," Yuan said.

Limitations of this study include difficulties standardizing electronic health data between different health systems, especially in different countries, necessitating the independent training and application of the AI model to different data sets. Additional analyses are also required to explicitly account for ethnic diversity. Further, prediction accuracy decreases with longer time intervals between risk assessment and cancer occurrence.

Funding of this study was provided by the Novo Nordisk Foundation, the National Institutes of Health, the Hale Family Center for Pancreatic Cancer Research, the Pancreatic Cancer Action Network, the Noble Effort Fund, the Wexler Family Fund, Promises for Purple, the Bob Parsons Fund, the Lustgarten Foundation, and Stand Up To Cancer (SU2C; The AACR is the Scientific Partner of SU2C). Yuan and Placido declare no conflicts of interest.

Davide Placido, Bo Yuan, Jessica X Hjaltelin, Amalie D Haue, Chen Yuan, Jihye Kim, Renato Umeton, Gregory Antell, Alexander Chowdhury, Alexandra Franz, Lauren Brais, Elizabeth Andrews, Aviv Regev, Peter Kraft, Brian M Wolpin, Michael Rosenthal, Søren Brunak, Chris Sander.
Pancreatic cancer risk predicted from disease trajectories using deep learning.
bioRxiv 2021.06.27.449937; doi: 10.1101/2021.06.27.449937

Most Popular Now

Unlocking the 10 Year Health Plan

The government's plan for the NHS is a huge document. Jane Stephenson, chief executive of SPARK TSL, argues the key to unlocking its digital ambitions is to consider what it...

Alcidion Grows Top Talent in the UK, wit…

Alcidion has today announced the addition of three new appointments to their UK-based team, with one internal promotion and two external recruits. Dr Paul Deffley has been announced as the...

AI can Find Cancer Pathologists Miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to...

New Training Year Starts at Siemens Heal…

In September, 197 school graduates will start their vocational training or dual studies in Germany at Siemens Healthineers. 117 apprentices and 80 dual students will begin their careers at Siemens...

AI, Full Automation could Expand Artific…

Automated insulin delivery (AID) systems such as the UVA Health-developed artificial pancreas could help more type 1 diabetes patients if the devices become fully automated, according to a new review...

How AI could Speed the Development of RN…

Using artificial intelligence (AI), MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies. After training...

MIT Researchers Use Generative AI to Des…

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research...

AI Hybrid Strategy Improves Mammogram In…

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection...

Penn Developed AI Tools and Datasets Hel…

Doctors treating kidney disease have long depended on trial-and-error to find the best therapies for individual patients. Now, new artificial intelligence (AI) tools developed by researchers in the Perelman School...

Are You Eligible for a Clinical Trial? C…

A new study in the academic journal Machine Learning: Health discovers that ChatGPT can accelerate patient screening for clinical trials, showing promise in reducing delays and improving trial success rates. Researchers...

Global Study Reveals How Patients View M…

How physicians feel about artificial intelligence (AI) in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich...

New AI Tool Addresses Accuracy and Fairn…

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms...