An Extracellular Vesicle Protein-Based Machine Learning Framework for Early Detection of Oesophageal Squamous Cell Carcinoma: A Multicentre, Prospective Study.

Early detection of oesophageal squamous cell carcinoma (ESCC) is critical for improving survival, yet current screening is hampered by the lack of effective, non-invasive methods. Here, we developed and prospectively validated an extracellular vesicle (EV) protein-based blood test for the preclinical detection of ESCC. We first engineered BarFlare, a high-sensitivity platform for serum EV protein analysis, and identified a novel biomarker panel that includes EV-associated squamous cell carcinoma antigen (SCC) and matrix metalloproteinase-13 (MMP13). These biomarkers were integrated with clinical factors into an interpretable multi-criteria decision-making classification fusion (MCF) machine-learning framework. The MCF model was trained and validated in prospective, multicentre diagnostic cohorts (n = 1018), and its preclinical detection capability was assessed in a prospective, population-based longitudinal cohort. The MCF framework accurately distinguished patients with ESCC from healthy controls in a test set (AUC, 0.987) and two external validation cohorts (AUCs, 0.926 and 0.960), including those with early-stage disease (AUCs, 0.901-0.980). Critically, in the longitudinal cohort, the framework identified individuals who would later develop ESCC from their baseline blood samples with a median lead time of 34.9 months (range, 0.4-72.5) before clinical diagnosis (AUC, 0.864; sensitivity, 73.3%; specificity, 82.2%). The risk score of the model correlated with time to diagnosis, and its dynamic increase significantly outperformed that of traditional serum SCC for preclinical risk stratification. Our validated, blood-based EV protein signature not only accurately detects prevalent ESCC but also identifies high-risk individuals years before clinical presentation, providing a powerful, non-invasive tool that supports risk-stratified screening and creates a critical window for early, potentially curative intervention. Trial Registration: Chinese Clinical Trial Registry identifiers: ChiCTR2200066733 and ChiCTR2200065610.