An AI flagged pancreatic cancer on scans an average 475 days before doctors did

A Mayo Clinic radiologist’s imaging model called REDMOD picked up signs of pancreatic cancer on routine abdominal CT scans an average of 475 days before the patients were clinically diagnosed, on scans that radiologists had read as showing no cancer. The result belongs to a retrospective validation study: the scans were pulled from past patients whose outcomes were already known, and nobody was screened with the tool in real time.

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The timeline matters for reading the 475-day figure. The paper went online in the journal Gut on April 22, 2026, Mayo Clinic’s news network published its account a week later, and a BMJ Group summary was carried by ScienceDaily on October 26. The sources describe a research model with prospective testing still ahead, and none of them describes an approved product or a clinical recommendation.

What REDMOD reads in a pancreas that looks normal

The BMJ Group summary of the Gut paper puts the model’s signal at an average of 475 days ahead of diagnosis. The input is an ordinary abdominal CT. REDMOD, short for Radiomics-based Early Detection MODel, automatically outlines the pancreas and measures hundreds of quantitative features of tissue texture and structure that the human eye does not register as a lesion, according to Mayo Clinic’s news network.

Ajit Goenka, a Mayo Clinic radiologist and the study’s senior author, framed the finding as a capability claim about appearance: “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.” He added that the greatest barrier has been the “inability to see the disease when it is still curable.”

The first-listed author on the Gut paper is Sovanlal Mukherjee. Funding came from the National Institutes of Health, the Hoveida Family Foundation, the Mayo Clinic Comprehensive Cancer Center and the Champions for Hope Pancreas Cancer Research Program of the Funk-Zitiello Foundation.

Seventy-three percent against thirty-nine, and the other side of the ledger

The research team assembled 219 patients with abdominal CT scans taken before a pancreatic ductal adenocarcinoma was found, every one of them read as cancer-free at the time. Of those, 87 scans (40 percent) came 3 to 12 months before diagnosis, 76 (35 percent) came 12 to 24 months before, and 56 (25 percent) came more than 24 months before, reaching back about three years. They were compared with 1,243 people matched on age, sex and scan date who stayed cancer-free for up to three years, and the model was then checked on an independent group of 539 patients from several hospitals, as News-Medical’s April report also lists.

REDMOD’s sensitivity was 73 percent, against 39 percent for experienced radiologists reading the same material. On the earliest scans, those taken more than two years ahead of diagnosis, the model detected 68 percent of cancers while radiologists detected 23 percent.

False positives are the harder number to pin down. In the 539-patient independent group, the model correctly classified just over 81 percent of cancer-free scans, and on the public NIH-PCT dataset of 80 patients it reached 87.5 percent. Simple arithmetic on the first figure leaves roughly 19 percent of cancer-free scans flagged in error, though the write-up reports no false-positive count and does not label the figure as specificity. Pancreatic cancer is uncommon in the general population, and the paper’s own title refers to a “low-prevalence setting”; in that setting, even a modest false-flag rate can mean many alarms for each true cancer, which is why the question carries weight.

On repeat scans of the same patients taken months apart, REDMOD returned matching results in 90 to 92 percent of cases.

A retrospective result and the limits its authors state

Several limits are written into the summary. The study population lacked sufficient ethnic diversity, which could affect how the model performs in other groups. Prospective testing is needed, particularly in people at elevated risk, such as those with unexplained weight loss or recently diagnosed diabetes. And the tool is described as not yet ready for routine clinical use. Reviewing scans after the fact also hands the model a cohort in which cancer is known to arrive within three years, a situation no live reader ever has.

Mayo Clinic says the next stage is a prospective study called Artificial Intelligence for Pancreatic Cancer Early Detection, or AI-PACED. It is designed to test how clinicians can fold AI-guided detection into the care of patients at elevated risk, following people over time for early detection, false positives and clinical outcomes. The model is intended to read CT scans already obtained for other reasons, with new-onset diabetes among the high-risk groups named.

The authors’ argument for the effort rests on survival arithmetic. They write that the timing of diagnosis is “the single most critical determinant of survival outcomes,” and their modelling suggests that raising the share of cancers found while still localized from 10 percent to 50 percent would more than double survival rates. Whether a 475-day average lead time in a retrospective cohort turns into a real shift in stage at diagnosis is the question AI-PACED is built to answer, and no result from it has been reported. The stakes behind an earlier flag are in the national statistics: the National Cancer Institute’s SEER program puts five-year relative survival for pancreatic cancer at 13.7 percent and reports that 51 percent of cases are already distant-stage at diagnosis.

This article was produced with the assistance of AI and reviewed by Morning Overview editors prior to publication.

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