Beyond sensitivity and specificity: Redefining the era connotation of "low-risk" in pancreatic cancer screening.

Pancreatic cancer remains a highly lethal malignancy, primarily due to late-stage diagnosis. Current screening paradigms, which focus exclusively on high-risk individuals, leave the vast "low-risk" population unscreened. This conventional binary risk stratification, based predominantly on family history and known genetic syndromes, fails to incorporate emerging risk dimensions such as polygenic risk scores, lifestyle factors, and novel biomarkers. We propose a paradigm shift from this static model towards a dynamic, multidimensional risk stratification framework. By integrating genetic susceptibility (, newly identified variants in , ), lifestyle metrics (, new-onset diabetes), and liquid biopsy biomarkers (, circulating tumor DNA, carbohydrate antigen 19-9 dynamics), we can reclassify a subset of "low-risk" individuals who may benefit from targeted screening. The integration of artificial intelligence for prospective validation, as seen in ongoing trials, is crucial for implementing this approach.