Raman and surface-enhanced Raman spectroscopy for intraoperative cancer diagnostics: sentinel lymph node biopsy,...

Sentinel lymph node biopsy (SLNB) serves as the gold standard for staging regional lymph node metastasis of solid tumors, which is crucial for guiding clinical treatment decisions and evaluating prognosis. Conventional intraoperative detection methods for SLN, such as frozen section and touch imprint cytology, have limitations including low sensitivity for micrometastasis, long detection time, and high subjectivity. Raman spectroscopy (RS), as a label-free, non-destructive optical molecular imaging technology, can obtain the biochemical fingerprint information of tissues by detecting the inelastic scattering of photons with biomolecules. A series of derivative techniques, exemplified by Surface-Enhanced Raman Spectroscopy (SERS), further overcome the shortcomings of limited penetration depth and weak inherent signals of RS, realizing ultra-sensitive and targeted detection of SLN. Crucially, the integration of advanced machine learning algorithms and deep learning workflows effectively addresses the multivariate complexity of high-dimensional Raman data, enabling rapid, objective, and automated screening for lymph node metastases. This review systematically summarizes the application progress of RS, SERS and derivative technologies in SLNB of various tumors (including breast cancer, thyroid cancer, melanoma, etc.), elaborates their technical principles, application forms (label-free and labeled detection), advantages and limitations in clinical practice, and discusses the current challenges and future development directions of RS technology in the clinical translation of SLNB, aiming to provide insights into the development of rapid, accurate and objective intraoperative SLN detection technology.