AI-Guided Additive Scoring Model for Differential Diagnosis of Primary Liver Cancer

This study provides a scientific rationale for integrating small extracellular vesicle (EV) phenotyping with conventional serum biomarkers to improve minimally invasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) in LR-M lesions. By translating a machine learning (ML)_derived hybrid model into a simple additive scoring system, we demonstrate how ML can yield clinically interpretable tools that bridge computational discovery and bedside application. The findings are particularly relevant for hepatologists, oncologists, and radiologists managing patients in whom imaging remains indeterminate and biopsy carries procedural risk. The five-point model can be applied using routine laboratory values, providing a paper-and-pencil diagnostic aid that requires no specialized software or hardware. While external multicenter validation is necessary given the cohort size, this approach illustrates how transparent, interpretable machine learning can support precision diagnostics in primary liver cancer.