Pancreatic cancer is among the deadliest forms of cancer, mostly because it is rarely detected early. According to studies, by 2030, it is projected to become the second-leading cause of cancer-related deaths in the United States and among the top ten globally. A major reason is that nearly 85 per cent of cases are diagnosed only after the disease has already spread, making treatment far more difficult. But a new artificial intelligence (AI) tool may soon change that narrative.

Experts say pancreatic cancer often develops silently. In its early stages, it produces few or absolutely no symptoms, and tumours may not be visible on standard imaging scans. By the time symptoms like abdominal pain, weight loss, or jaundice appear, the disease is often advanced. This delay in diagnosis has long been the biggest barrier to improving survival rates. As experts note, detecting the disease when it is still curable has remained a major challenge until now.

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Researchers from theMayo Clinicand theUniversity of TexasMDAnderson Cancer Centerhave developed a powerful AI system known as REDMOD, or Radiomics-Based Early Detection Model.

Unlike traditional methods that look for visible tumours, REDMOD analyses radiomic patterns, tiny changes in tissue texture and structure within CT scans that are often invisible to the human eye. These subtle signals can appear years before a tumour becomes detectable.

Pancreatic cancer has low survival rates as in most cases the diagosis comes very late

The AI model was trained on 969 CT scans and then tested on additional datasets. The results are promising:

Cancer often begins with genetic mutations that slowly alter how cells grow and divide. These changes can take years to form a detectable tumour. REDMOD’s strength lies in spotting these early, hidden alterations before they evolve into visible cancer.

For patients, experts believe this could be life-changing. Detecting pancreatic cancer earlier increases the chances of effective treatment, including surgery and targeted therapies.

While the results are encouraging, the technology is not perfect. In the study, some healthy individuals were incorrectly flagged as high-risk, meaning further testing would be required to confirm diagnosis.

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