Mayo Clinic has announced the development of an artificial intelligence (AI) model capable of detecting pancreatic cancer on routine abdominal CT scans up to three years before clinical diagnosis. This breakthrough, detailed in an announcement on Wednesday, leverages subtle radiomic signatures that appear before tumors are visible, potentially enabling curative treatment for one of the deadliest cancers.
The findings, published in the journal Gut, build on Mayo's multiyear research, with prior models achieving detection around 475 days ahead. The new AI system, named the Radiomics-based Early Detection Model (REDMOD), was validated using data and workflows mirroring clinical practice, including CT scans from multiple institutions, imaging systems, and protocols.
Researchers analyzed nearly 2,000 CT scans originally interpreted as normal, including scans from patients later diagnosed with pancreatic cancer. REDMOD identified 73% of these prediagnostic cancers at a median of about 16 months before diagnosis, nearly double the detection rate of specialists reviewing the same scans without AI assistance. The advantage was even greater for scans obtained more than two years before diagnosis, where the AI identified nearly three times as many early cancers that would otherwise go undetected.
Pancreatic cancer remains one of the deadliest cancers due to its lack of detectable signs in early stages. According to the National Cancer Institute, more than 85% of patients are diagnosed after the disease has spread, and five-year survival rates remain below 15%. Projections indicate it will become the second-leading cause of cancer-related death in the U.S. by 2030.
Dr. Ajit Goenka, the study's senior author and a Mayo Clinic radiologist and nuclear medicine specialist, stated: "The greatest barrier to saving lives from pancreatic cancer has been our inability to see the disease when it is still curable. This AI can now identify the signature of cancer from a normal-appearing pancreas, and it can do so reliably over time and across diverse clinical settings."
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