Strategies and tools for enhancing early detection of pancreatic cancer

Pancreatic adenocarcinoma (PAC) remains the most lethal malignancies, with a 5-year survival rate of only around 12%. The dismal prognosis is largely attributable to late diagnosis, as early PAC is frequently asymptomatic and radiologically inconspicuous. Contemporary international guidelines recommend surveillance for well-defined high-risk groups, particularly carriers of pathogenic germline mutations such as BRCA2, STK11, and CDKN2A, as well as familial pancreatic cancer kindreds. In these settings, endoscopic ultrasound and magnetic resonance imaging are the recommended surveillance modalities. Recent advances in biomarker discovery, including circulating tumor DNA, methylation signatures of cell-free DNA, and extracellular vesicles, have demonstrated encouraging results, particularly when combined with CA19-9. Paraneoplastic phenomena such as pancreatic cancer-associated diabetes and cancer-induced cachexia provide additional opportunities for earlier clinical recognition. Parallel progress has been achieved in artificial intelligence-driven radiomics analysis and deep learning algorithms, which can increase the sensitivity of cross-sectional imaging for small or otherwise occult lesions. The integration of clinical risk stratification, molecular biomarkers, and AI-enhanced imaging represents a promising framework to shift diagnosis toward surgically resectable stages. This review synthesizes the current evidence, highlights recent advances, and discusses practical considerations for physicians to facilitate earlier detection of PAC.