Advances in Early Detection of Pancreatic Ductal Adenocarcinoma: Biomarkers, Imaging, and Artificial Intelligence for Translational Diagnostics

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies, largely due to late-stage diagnosis and lacks effective early detection procedures. Despite advances in the discovery of biomarkers, imaging technologies, and artificial intelligence, clinically scalable frameworks for detection of early PDAC have not yet emerged. This minireview evaluates the current diagnostic approaches for PDAC including serum biomarkers, cross-sectional imaging, invasive diagnostic procedures, and emerging non-invasive strategies. We further synthesize the recent developments in liquid biopsy and multi-omics profiling and AI-assisted diagnostics which enable the detection of molecular and radiographic features in association with PDAC. We argue that the principal barrier to reaching improved PDAC outcomes is not the lack of diagnostic innovation, but that fragmented advancements do not translate into integrated, scalable, multi-modal diagnostic frameworks. Advancing such integrated detection strategies may enable diagnosis at earlier, potentially curable stages and ultimately improve patient outcomes.