Hao Shen, Huancai Yin, Xia Huang, Guangxing Liu, Jian Yin
Surgical resection requires precise intraoperative margin assessment. Label-free Raman techniques, particularly surface-enhanced Raman scattering and stimulated Raman scattering, enable real-time guidance by detecting cancer-specific spectral signatures linked to protein-to-lipid shifts and oncometabolite accumulation. Despite accuracy exceeding 90%, clinical translation faces barriers: regulatory approval, cost-effectiveness, in-vivo probe limitations, standardized definitions, and AI interpretability. Overcoming these requires multicenter collaborations to generate safety data, establish standardized protocols, and train explainable AI algorithms. This review summarizes technological advances, outlines characteristic spectral features across cancers, and critically analyzes hurdles to clinical integration, paving the way for Raman spectroscopy to transform precision oncologic surgery by balancing complete resection with maximal tissue preservation.