ArticleJournal of extracellular vesicles2026
An Extracellular Vesicle Protein-Based Machine Learning Framework for Early Detection of Oesophageal Squamous Cell Carcinoma: A Multicentre, Prospective Study.
Article in Journal of extracellular vesicles, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
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Authors and funding
24 authors.
Funding
Abstract
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.
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