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

ABSTRACT:

Background & aims

In our 2017 publication, we introduced a novel concept for liver cancer detection using large extracellular vesicles (EVs), specifically AnnV⁺EpCAM⁺ASGPR1⁺ 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In the present study, we revisited this legacy dataset - now supplemented with unpublished but previously collected measurements (N = 166) - and applied modern artificial intelligence-assisted analytical strategies.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

SUBMITTER: Willms AG 

PROVIDER: S-EPMC12538156 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature