Mukherjee, Sovanlal

Background Failure of conventional imaging to detect pancreatic ductal adenocarcinoma (PDA) at its visually occult pre-diagnostic stage is a primary barrier to improving its otherwise poor rate of survival. Objective To develop and validate the Radiomics-based Early Detection MODel (REDMOD), an AI framework to identify subvisual radiomic signatures of pre-diagnostic PDA on standard-of-care CT. Designs REDMOD was trained on a multi-institutional cohort (n=969; 156 pre-diagnostic, 813 control) and tested on an independent set (n=493; 63 pre-diagnostic, 430 control), simulating a low prevalence (~1:6) early detection paradigm. The fully automated framework couples AI-driven segmentation with a heterogeneous ensemble architecture trained on a 40-feature radiomic signature derived from Synthetic Minority Over-sampling Technique (SMOTE)-balanced data. A tunable Youden Index-optimised classification threshold enables performance calibration without retraining. Validation included direct comparison with radiologists, longitudinal test–retest analysis and external specificity validation across two independent cohorts (n=539 and n=80). Results On an independent test set (n=493), REDMOD identified occult PDA (AUC 0.82; 73.0% sensitivity) at a median 475-day lead time. This represented nearly twofold higher sensitivity than radiologists (38.9%; p<0.001), which grew to nearly threefold (68.0% vs 23.0%) at >24 months lead time. REDMOD showed strong longitudinal stability (90–92% concordance) and generalisable specificity across multi-institutional (81.3%; n=539) and public (87.5%; n=80) datasets. Mechanistic analyses confirmed predictive power derived principally from multi-scale wavelet-filtered textural features (90% of selected signature), which outperformed unfiltered features (AUC 0.82 vs 0.74; p=0.007) in capturing subvisual architectural disruptions. Conclusions REDMOD is an automated, mechanistically grounded, longitudinally stable, externally validated AI that surpasses radiologists for PDA detection at its visually occult pre-diagnostic stage. These attributes position it for prospective validation in high-risk cohorts, a necessary step towards shifting the paradigm from late-stage symptomatic diagnosis to proactive pre-clinical interception.

Abstract Background: Early detection is the most potent strategy to improve survival in PDA, but its success hinges on an unresolved question: does radiologically invisible disease represent biologically early or indolent disease, or established malignancy beneath the threshold of detection? We leveraged prediagnostic CTs—incidental scans obtained 3-36 months prior to diagnosis without a focal mass—to define the relationship between imaging phenotype and tumor biology. Methods: From a multi-institutional pool of 5,046 biopsy-confirmed PDAs, we assembled a cohort of 346 prediagnostic CTs, which were independently evaluated by three radiologists using a standardized protocol. Based on majority vote, CTs were dichotomized into imaging-occult (IO) (normal pancreas) and imaging-apparent (IA) (abnormal pancreas with indirect signs such as focal atrophy, duct dilatation, etc.) groups. Tumor size and metastases (M) at diagnosis, and overall survival (OS) were compared using multivariable linear (size), logistic (M1), and Cox proportional hazards (OS) models, adjusting for lead time to diagnosis, age, sex, diabetes, CA 19-9, and surgical resection status, to isolate the independent biological relevance of the imaging phenotype. Results: IO phenotype was seen in 150 patients (mean age 66.7±11.2; 37.3% females) while 196 had IA phenotype (mean age 69.9±10.6; 49.5% females). Median lead time was 396 days (range: 90-1088) with the IA group being enriched in 3-12-month lead time (57.7% vs 33.3%; p&lt;0.001). Both groups had comparable diabetes rates (IA 33.2% vs IO 32.7%; p=1.0), median CA 19-9 (IA 193 vs IO 322 U/mL; p=0.41), and surgical resection (IA 38.3% vs IO 38.0%; p = 1.0). While unadjusted mean tumor size at diagnosis was larger in IA group (3.8 cm vs 3.2 cm), there was no difference after adjusting for clinical factors (-7.1% difference; 95% CI: -17.2, 4.2; p=0.21). Metastases were also comparable (M rate: IA 22.4% vs IO 27.3%; adjusted OR 0.97; 95% CI: 0.50, 1.88; p=0.93). Adjusted Cox models demonstrated equivalent OS (median: 18.7 vs 17.6 months; HR 1.06; p=0.66), which persisted even when stratified by surgical status (HR 1.07; 95% CI: 0.83, 1.38; p=0.61). Even in the critical 3-12 months lead time (n=163), OS remained equivalent (HR 0.89; p=0.62), and groups did not differ in either tumor size (-6.4%; 95% CI: -22.4, 12.9; p=0.49) or metastases (OR 0.48; 95% CI 0.14, 1.59; p=0.23). Conclusions: A normal-appearing pancreas on prediagnostic imaging is common yet signifies neither early nor indolent disease—it harbors PDA biologically equivalent to the imaging-apparent pancreatic phenotype. Because the disease at this stage lies beyond human perceptual capacity, AI augmentation represents the only potential path to realizing the survival benefit of early detection. Citation Format: Khurram Khaliq Bhinder, Sovanlal Mukherjee, Armin Zarrintan, Angela Ammirabile, Ahmed Khan Jadoon, Ziding Feng, Suresh T. Chari, Ajit H. Goenka. Dissociation between prediagnostic imaging phenotype and tumor biology defines the early detection challenge in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB205.

Pancreatic cancer (PC), characterized by a dismal 5-year survival rate of approximately 12%, remains one of the most aggressive cancers. Majority of cases are identified at advanced stages, which are beyond the scope of surgical resection. Importantly, while the overall prognosis for PC is poor, localized stages of the disease can significantly enhance survival chances because of the potential for curative interventions. We explore the evolution of early detection strategies, including screening methods for high-risk individuals, the yield of diagnostic tests, and the impact of emerging technologies. Special emphasis is given to the role of artificial intelligence in overcoming the limitations of standard imaging practices, thereby refining diagnostic accuracy, and predicting patient outcomes. Our review synthesizes current controversies and addresses the technological advancements that could potentially shift the paradigm toward improved survival rates for patients with PC.