An artificial intelligence (AI) model developed at the Mayo Clinic can detect very early signs of pancreatic cancer from CT scans of the abdomen that are normally invisible to the human eye.
Researchers tested the Radiomics-based Early Detection MODel (REDMOD) and found it was able to identify 73% of very early pancreatic ductal adenocarcinoma. In contrast, only 39% of these cases were identified by radiologists.
Around 67,530 Americans are expected to be diagnosed with pancreatic cancer in 2026. It has a very poor prognosis, around 13% survival at five years, largely because it is often diagnosed at a late stage.
“Early detection of pancreatic ductal adenocarcinoma is the most powerful approach to improve survival,” write lead author Ajit Harishkumar Goenka, MD, a researcher and clinician at the Mayo clinic, and colleagues in the journal Gut.
“However, this objective is fundamentally impeded by the morphologically normal appearance of the pancreas on conventional imaging during its curable pre-clinical phase.”
To try and tackle the issue of early diagnosis, Goenka and team developed REDMOD to analyze very early signs of pancreatic cancer on computed tomography (CT) scans that are normally very hard to detect with the human eye.
As part of the study the researchers evaluated 219 routine CT scans of the abdomen from patients who later developed pancreatic cancer. They also included 1243 matched controls scans of people who had not developed cancer.
After the AI model was developed and trained the researchers tested it in 63 pre-diagnostic cases and 430 controls. These scans were taken around 16 months before the cancer cases were diagnosed.
REDMOD correctly identified 73% people who would later develop pancreatic cancer, while radiologists looking at the same scans only picked up about 39% of cases. The ability of the model to identify people without the disease was also good and it correctly identified 88% of people who did not have pancreatic cancer. When patients had repeat scans, the AI’s risk score was very consistent, agreeing 90–92% of the time.
“The demonstrated ability of the framework to consistently detect these occult signals on a large clinically oriented dataset, combined with its high longitudinal stability and validated specificity, establishes a robust foundation for AI-augmented early detection,” write Goenka and colleagues.
“While prospective validation is paramount to confirm clinical utility, the REDMOD framework represents a significant advance towards shifting the paradigm for sporadic pancreatic ductal adenocarcinoma from a late-stage symptomatic diagnosis to proactive pre-clinical interception, offering tangible hope for improving outcomes in this challenging disease.”
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