ArticleJHEP reports : innovation in hepatology2025
What we missed then, AI sees now: Revisiting legacy large extracellular vesicle data to reveal synergistic biomarkers for liver cancer screening.
Article in JHEP reports : innovation in hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Extracellular vesicle-mediated bidirectional communication between the brain and peripheral organs in Alzheimer's disease: evidence, mechanisms, and translational perspectives.Journal of nanobiotechnology · 2026Review
- AI-guided additive scoring model for differential diagnosis of primary liver cancer.JHEP reports : innovation in hepatology · 2026Article
- Stratified management of residual gastric cancer risk afterFrontiers in microbiology · 2026Article
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Authors and funding
13 authors.
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Abstract
Background & Aims: In our 2017 publication, we introduced a novel concept for liver cancer detection using large extracellular vesicles (EVs), specifically AnnV Methods: In the present study, we revisited this legacy dataset - now supplemented with unpublished but previously collected measurements (N = 166) - and applied modern artificial intelligence-assisted analytical strategies. We evaluated a wide range of combinatorial models using Random Forest and Decision Tree classifiers, incorporating both rare large EV populations and classical serological markers (AFP, CEA, CA19-9, bilirubin). Results: The RF model combining AnnV Conclusions: This study demonstrates how legacy data, when re-analyzed with artificial intelligence-supported tools, can reveal clinically actionable insight. Large EVs - particularly derived from rare progenitor-like subpopulations - combined with classical serum markers, provide a promising non-invasive screening approach. Impact and implications: This study revisits a legacy extracellular vesicle dataset using modern artificial intelligence-assisted analysis to identify synergistic biomarker combinations for liver cancer screening. The results demonstrate that archived data, when re-analyzed with advanced computational tools, can yield novel and clinically relevant insights - particularly for screening liver malignancies. This approach may serve as a reproducible and transparent blueprint for similar efforts in liver diseases, oncology, and biomedical research more broadly. These findings are relevant to clinicians, translational researchers, and policymakers aiming to advance precision screening and early detection strategies. More broadly, this work underscores the role of artificial intelligence as a scientific collaborator and offers a model for responsible, sustainable innovation in biomedical research.
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