AI detects pancreatic cancer from CT scans

Mayo Clinic's REDMOD model was trained by taking CT scans from patients who were later diagnosed with pancreatic cancer, then returning to that same patient's earlier scans - taken before any diagnosis - to teach the system the subtle tissue patterns that preceded the eventual disease, according to Dr. Matthew Callstrom, Mayo's chair of radiology and medical director of AI strategy.

Under the hood, REDMOD - whose name stands for Radiomics-based Early Detection Model ^[6]- is a heterogeneous ensemble that combines logistic regression, random forest, and extreme gradient boosting (XGBoost) through a soft-voting mechanism. The model relies on 40 key features selected from roughly 968 to 1,000 extracted radiomic features, and about 90% of the features that mattered most came from wavelet-filtered versions of the scans rather than the raw images ^[6][7].

In the validation study, REDMOD identified pancreatic cancer signatures in 73% of patients who later developed the disease, compared with 39% detected by radiologists reviewing the identical scans ^[2]. The model flagged pre-clinical disease an average of 475 days - about 16 months - before clinical diagnosis, and on scans taken more than two years before diagnosis its accuracy held at 68% versus just 23% for radiologists ^[3].

On specificity, REDMOD posted 88% in the primary analysis, with 90-92% reproducibility when the same patient was scanned again ^[3][4]. In a separate independent validation cohort, specificity came in at 81.1% (95% CI 75.2%-93.1%), and on an external NIH-PCT dataset specificity reached 87.5% ^[6][7]- different cuts of the same underlying study rather than conflicting figures.

Pancreatic cancer has a five-year survival rate of just 13%, one of the lowest of any major cancer, largely because it is usually caught only after it has already spread ^[2]. Dr. Elliot K. Fishman, professor of radiology at Johns Hopkins Medicine, frames the stakes directly: 'If we can move the moment of diagnosis forward by even a year, we move patients from a conversation about managing a disease to a conversation about curing it.' Fishman notes that roughly 85% of pancreatic cancer patients are currently diagnosed too late for life-saving treatment, and that up to 40% of CT scans containing a tumor 2cm or smaller go undetected by human readers. An estimated 67,530 new pancreatic cancer diagnoses are expected this year ^[4], which is the scale of the population that earlier detection - even by months - could eventually reach.

REDMOD isn't the first AI system aimed at pancreatic cancer. Alibaba's DAMO Academy unveiled PANDA in November 2024, a tool that detects pancreatic ductal adenocarcinoma from standard non-contrast CT scans, and it has already screened more than 180,000 real-world scans since launch ^[5]. That gives PANDA a real-world deployment head start, while REDMOD - still moving into the prospective AI-PACED trial - remains earlier on the path to routine clinical use ^[1]. The two systems reflect different bets: PANDA optimized for broad screening already running at scale, REDMOD optimized for retrospective sensitivity on scans radiologists had already cleared, with more rigorous prospective validation still ahead.

Not everyone treats REDMOD as a settled win. Tatjana Crnogorac-Jurcevic, professor of molecular pathology and biomarkers at Queen Mary University of London and independent of the study, praised the study design but cautioned that population-wide screening for pancreatic cancer remains impractical given how rare the disease is ^[6]. Her own framing leans toward targeted use: 'There are defined high-risk groups for which surveillance will be possible,' and she sees the tool as one part of a broader diagnostic toolkit - 'Having an AI imaging tool to combine with our body fluid biomarkers would be fantastic' ^[8].

Mayo Clinic itself framed the finding for a broader audience on X, describing REDMOD as identifying disease signs "before tumors are visible, when curative treatment may still be an option" - a more confident public framing than the hedged academic tone of the underlying paper. Some commentary on X, though not tied to a specific named source, questioned whether earlier AI detection actually translates into a survival benefit, rather than simply moving up the date of diagnosis without changing the outcome.

Reddit commentary raised more practical concerns: whether a model trained and validated largely within Mayo's own patient population will generalize to other health systems without overfitting to that cohort, and whether an AI flag on a scan will reliably translate into affordable, accessible follow-up care and biopsies for patients outside major academic medical centers.