Machine Learning-Enabled Surface-Enhanced Raman Scattering for Label-Free Tracking of Hepatic Fibrosis Progression

Accurate monitoring of liver fibrosis (LF) is critical for the clinical management and prognostic evaluation of liver diseases. However, it remains challenging to develop a reliable and efficient method for tracking LF progression and identifying its etiological characteristics. Herein, we present an innovative strategy combining label-free surface-enhanced Raman scattering (SERS) technology and advanced machine learning (ML) for the etiological tracking and staging diagnosis of LF. Specifically, a highly sensitive, uniform Au cluster-structured nanoarray (AuCSNAs) substrate was fabricated via reactive ion etching (RIE) and gas-liquid interface self-assembly, obtaining high-quality serum SERS spectra from carbon tetrachloride (CCl4)-, dimethylnitrosamine (DMN)-, and thioacetamide (TAA)-induced LF mice at weeks 0, 3, and 6. Subsequently, the principal component analysis (PCA)-Meta-learning Random Forest-Lite (Meta-RF-Lite) model was constructed to extract, identify, and analyze these spectra. The PCA-Meta-RF-Lite model realized robust discrimination of SERS spectra from distinct LF etiologies and pathological stages, which can reach an accuracy of up to 98.3%, a sensitivity of up to 100.0%, a specificity of up to 98.7%, and an AUC of up to 0.989. Collectively, these results verify that the AuCSNAs/PCA-Meta-RF-Lite strategy enables reliable tracking and etiological differentiation of LF.