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

Figure 2 Dynamic risk stratification model based on multi-dimensional data integration. This framework enables continuous acquisition of multidimensional data: Whole-genome polygenic risk scores, circulating tumor DNA/carbohydrate antigen 19-9 dynamics, and new-onset diabetes or lifestyle modifications. An artificial intelligence engine integrates these non-linear variables to generate a quantitative risk score that updates over time. Individuals are stratified in real-time into average-, medium-, or high-risk categories, triggering tiered interventions. Feedback from each downstream test or clinical event refines individual risk profiles and recalibrates the model. This adaptive design transforms screening from a disposable qualification process into a dynamic, evidence-based monitoring continuum, promising earlier detection while minimising over-testing. ctDNA: Circulating tumor DNA; KRAS: Kirsten rat sarcoma viral oncogene homolog; CA19-9: Carbohydrate antigen 19-9; MUC1: Mucin 1; BMI: Body mass index; AI: Artificial intelligence; EUS: Endoscopic ultrasound; MRI: Magnetic resonance imaging.