What we missed then, AI sees now: Revisiting legacy large extracellular vesicle data to reveal synergistic biomarkers for liver cancer screening.

BACKGROUND & AIMS: In our 2017 publication, we introduced a novel concept for liver cancer detection using large extracellular vesicles (EVs), specifically AnnVEpCAMASGPR1 tumor-associated microparticles. Despite promising biology, the diagnostic utility was limited by the analytical tools available at the time, resulting in modest performance (AUC 0.70, 75% sensitivity, 47% specificity).

METHODS: In the present study, we revisited this legacy dataset - now supplemented with unpublished but previously collected measurements (N = 166) - and applied modern artificial intelligence-assisted analytical strategies.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 AnnVEpCAMCD133gp38 large EVs with classical serological markers achieved a mean accuracy of 88.2%, recall of 91.6%, and F1-score of 87.0% across 10 stratified train-test runs. This substantially outperformed earlier analysis efforts. To support clinical translation, we additionally developed a simplified decision tree model based on the same marker inputs, offering a visual and rule-based alternative that remained robust, with an average accuracy of 86.6% and recall of 87.3% and a sensitivity of 94% and a specificity of 78% (70/30 split) across 10 stratified train-test runs.

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.

© 2025 The Authors.

PMID: 41127881