From radiomics to transformers in pancreatic cancer detection and prognosis

Introduction: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies, primarily due to late diagnosis and poor therapeutic response. Advances in artificial intelligence (AI), particularly in medical imaging and multi-modal data integration, have created new opportunities for improving early detection and personalized prognostication. Methods: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. The protocol was prospectively registered with the Open Science Framework, covering studies published between 2015 and 2025. Results: Distinct from prior surveys that focus narrowly on specific algorithms or data types, this work introduces a generational taxonomy of AI approaches—ranging from classical radiomics-based machine learning to deep learning and contemporary transformer-based models—and maps their application to core clinical tasks such as detection, segmentation, classification, and outcome prediction. A key contribution is the integration of diverse datasets across imaging, pathology, and molecular sources; we further assess trends in availability, usage, and sample scale. Discussion: We critically evaluate limitations in generalizability, external validation, model calibration, and translational readiness, and outline recommendations for multi-center validation, standardized reporting, domain adaptation, and clinician-centered interpretability. Systematic review registration: https://doi.org/10.17605/OSF.IO/2DVHJ.