This study by Yin et al. (2025) developed and validated a machine learning model using cell-free DNA (cfDNA) fragmentomics for early detection of pancreatic ductal adenocarcinoma (PDAC). Plasma samples from over 1,100 participants were analyzed with shallow whole-genome sequencing to capture fragment size, copy-number changes, methylation profiles, and mutational signatures. These features were integrated into an ensemble model that achieved very high accuracy, with AUC values near 0.99 in both training and validation cohorts. The approach maintained strong sensitivity and specificity in external validation sets, including in patients with benign cystic lesions, and importantly, detected early-stage PDAC and CA19-9–negative cases that traditional biomarkers often miss. The results suggest that cfDNA fragmentomics offers a reliable, noninvasive method for identifying PDAC at a surgically treatable stage, with potential to reduce mortality by enabling earlier intervention. Because the method requires only shallow sequencing, it could be cost-effective and scalable for wider clinical use. Beyond pancreatic cancer, the study also demonstrates a framework that may be adaptable to other cancers where early detection is critical for survival.