Pancreatic cancer remains a highly lethal malignancy due to late-stage diagnosis and limited therapeutic options. This study presents the development and validation of a noninvasive circulating cell-free DNA (cfDNA)-based model for early pancreatic cancer detection. In a case-control study comprising 232 patients with pancreatic cancer and 235 healthy controls, the model demonstrated high diagnostic accuracy (AUC = 0.9799 in training; 0.9622 in validation). A prospective cohort study involving 1,926 individuals with diabetes and obesity established risk factors for pancreatic cancer and further assessed its clinical applicability. The model detected 75% of pancreatic cancer cases, including all stage 0 patients, with a lead time of up to 298 days, significantly outperforming CA19-9. Additionally, it demonstrates potential for distinguishing high-risk from low-risk pancreatic cysts, thereby facilitating more precise risk stratification. This study highlights the potential of cfDNA-based screening as a scalable, noninvasive tool for early pancreatic cancer detection, warranting further large-scale clinical validation to enhance patient outcomes.
Significance: This study develops a cfDNA-based model for early pancreatic cancer detection, demonstrating high accuracy and prospective clinical validation. By enabling presymptomatic identification and risk stratification, this noninvasive approach enhances early intervention and improves outcomes, supporting potential clinical applicability and representing a meaningful step toward improving pancreatic cancer screening and management. See related commentary by Tsui and Lo, p. 10.
©2025 American Association for Cancer Research.