AI Model Could Flag Pancreatic Cancer Risk up to Five Years Before Diagnosis

Key Takeaways:

• Mayo Clinic researchers have developed an artificial intelligence (AI) model that uses routinely collected electronic health record data to identify people at increased risk of pancreatic cancer up to five years before the disease becomes clinically apparent.
  
• The model’s predictions were strongly informed by routine blood tests, particularly components of the complete blood count, alongside expected associations such as diabetes and pancreatitis, as well as patterns in seemingly unrelated healthcare interactions.
  
• The findings, presented at the American College of Surgeons (ACS) Clinical Congress 2026, have not yet been peer-reviewed, and further prospective validation is needed before the model could be considered for routine clinical use.
  
  
  

A cancer with a disproportionately high mortality burden

Pancreatic cancer is relatively uncommon, but it carries a disproportionately high mortality burden. In the United States, estimates suggest that approximately 67,530 people will receive a diagnosis in 2026, and 52,740 will die from the disease.

Although pancreatic cancer accounts for roughly 3% of all new cancer cases, it is responsible for approximately 8% of cancer deaths, underscoring its particularly high mortality burden.

Pancreatic cancer can be difficult to detect, and many cases are diagnosed only after the cancer has spread. An early diagnosis could be particularly beneficial: data show a five-year relative survival of about 44% when the cancer is still localised, compared with just 3% when it has spread to distant parts of the body.

Now, research suggests that an AI model could identify people at increased risk of pancreatic cancer as early as five years before it becomes clinically apparent, using information routinely collected in electronic health records.

The findings were presented by Mayo Clinic researchers at the ACS Clinical Congress 2026 in Washington DC. As such, it is important to note that the findings have not yet been peer-reviewed.

Early pancreatic cancer clues in routine health data

The researchers developed the model using longitudinal health information from the Mayo Clinic health system. According to the press release, this involved electronic health records for almost 40,000 patients, along with results from routine laboratory tests collected over many years.

The dataset included 6,066 people who went on to develop pancreatic cancer and 33,396 people who served as controls. Participants had between 7.5 and 19 years of clinical history available for analysis. The researchers’ aim was to determine whether patterns in routine medical data could reveal subtle signs associated with an increased future risk of pancreatic cancer.

Pancreatic cancer can develop over many years. However, the clinical signs that would prompt a doctor or a person to investigate the disease may not appear until much later.

Because pancreatic cancer is relatively uncommon in the general population, routinely screening everyone for the disease is not currently considered feasible. An approach capable of identifying people at elevated risk might therefore allow further evaluation to be focused on this group.

“The most important finding was that subtle patterns across a patient’s healthcare journey could reveal elevated pancreatic cancer risk up to five years before the diagnosis was first documented,” lead study author Chris Varghese, MBChB, surgical data analyst at Mayo Clinic, told Medical News Today.

“This creates a potential window to move detection upstream – from reacting to symptoms to proactively identifying patients who may benefit from earlier, targeted screening or further evaluation – an approach that has historically been difficult because pancreatic cancer is relatively rare and existing screening tests are not practical for broad population use.”

How accurately did the AI model identify risk?

The researchers assessed the model’s ability to distinguish between people who would later develop pancreatic cancer and those at lower risk.

Varghese informed Medical News Today that the abstract has since been updated with more data. Five years before a pancreatic cancer diagnosis, the model achieved an area under the receiver operating characteristic curve (AUROC) of 0.853 in a slightly smaller cohort of patients. The updated figures are 0.84 at one year, 0.80 at two years, and 0.76 at three years prior to diagnosis.

AUROC values range from 0.5, which indicates performance no better than chance, to 1.0, which represents perfect discrimination.

The model also achieved an area under the precision-recall curve (AUPRC) of 0.712. This measure can be particularly useful when studying relatively uncommon outcomes such as pancreatic cancer.

The researchers additionally assessed calibration – how closely the model’s predicted risk corresponded to what actually happened in the study population. The reported slope of the calibration plot was 1.08.

“An AUROC in this range means the model is reasonably good at distinguishing people who are at higher risk of pancreatic cancer from those who are at lower risk,” Varghese explained to Medical News Today. “But for clinical use, calibration – whether the risk predicted by the model actually matches the rate of pancreatic cancer we observe in practice – is also very important.”

“Given these promising results, and ongoing research that may further improve performance using more advanced AI models, the next challenge is determining what level of predicted risk should trigger additional evaluation. That threshold may differ depending on the downstream test being considered, its risks and costs, and the potential benefit of detecting cancer earlier.”

“Defining those thresholds and testing how the model performs within a real screening pathway are important next steps,” Varghese said.

In the press release, the researchers offered an example of how the predicted risk could potentially be interpreted: among people for whom the model estimated a greater than 50% risk of pancreatic cancer, 88% were diagnosed with the disease within a year.

Importantly, these results describe the model’s performance within the research dataset. They do not yet establish that using the AI system in routine clinical care will improve pancreatic cancer detection or outcomes for people living with the disease.

What signals did the model rely on?

“We found that the model’s predictions were strongly informed by routine blood tests, particularly components of the complete blood count, which are often obtained for other reasons or as part of routine care,” study co-author Cornelius Thiels, DO, MBA, surgical oncologist at Mayo Clinic, told Medical News Today.

“Expected clinical associations such as diabetes, pancreatitis, and other pancreatic conditions were also important. In addition, patterns in seemingly unrelated healthcare interactions contributed useful information. None of these signals alone would be sufficient to suggest a future diagnosis of pancreatic cancer, but the AI model was able to learn from how they appeared and changed over time across a patient’s longitudinal healthcare record.”

Could the model be used in clinical practice?

The current findings represent an early step and are not evidence that the AI model is ready for use as a pancreatic cancer screening test.

The researchers say they designed the system to rely primarily on information that is already routinely collected in healthcare settings.

The team is now moving the model beyond retrospective analysis and into prospective research at Mayo Clinic. They also plan to evaluate the model at a non-Mayo healthcare system and are investigating newer machine-learning approaches that may further improve its performance.

Prospective validation will be important, as an AI model can perform differently when applied to people outside the dataset on which it was developed. Researchers will also need to establish how the model performs across different healthcare systems and patient populations.

As risk prediction tools of this kind move closer to practice, healthcare professionals will increasingly need to understand how to interpret measures such as discrimination and calibration, and how to judge whether an AI system is safe and appropriate to use. The College of Contemporary Health’s AI Essentials for Primary Care: Tools, Ethics and Everyday Applications course is designed to support the whole primary care team in evaluating AI systems critically and explaining their role in care to the people they support.

What happens to people identified as being at higher risk?

Another important question is what should happen once someone has been identified as being at elevated risk. A prediction tool could potentially lead to additional testing or imaging, but any such follow-up would need to balance the potential benefit of earlier cancer detection against the risks, costs, and anxiety associated with false-positive results.

If further studies confirm that the model can reliably identify people who are likely to develop pancreatic cancer before symptoms or conventional clinical signs emerge, it could eventually give clinicians another tool for deciding who may benefit from closer monitoring or further evaluation.

“Identifying what should happen after someone is flagged as high risk is one of the most complex and important next steps, and we do not yet know the optimal approach,” Thiels explained to Medical News Today.

“Our hope is that by first identifying a smaller, higher-risk population, downstream screening tests – such as CT, MRI, endoscopic ultrasound, or blood-based biomarker tests – may perform better and become more practical. We and others are actively working to determine how best to combine digital risk prediction with these downstream tests and to validate those strategies prospectively before they are ready to be rolled out to patients.”

CCH insight

AI-driven risk prediction tools, such as the pancreatic cancer model developed at Mayo Clinic, are a reminder that AI is increasingly shaping how risk is identified and how care pathways may be designed. Knowing how to assess these tools – their accuracy, calibration, accountability and data protection implications – is fast becoming a core skill for healthcare professionals.

Our AI Essentials for Primary Care: Tools, Ethics and Everyday Applications course is a 3.5-hour online CPD course offering clear, NHS-aligned guidance for nurses, pharmacists, physician associates, healthcare assistants, practice managers and administrative staff. You will learn how to evaluate AI systems safely, understand accountability and data protection requirements, and gain the confidence to give patients clear answers about AI in their care.

Explore AI Essentials for Primary Care →

Source: MedicalNewsToday