Raman Spectroscopy Applications in Cancer Diagnostics

Raman spectroscopy is a molecular optical technique that probes inelastic scattering to obtain vibrational fingerprints of biological tissues. Over recent decades, this label-free, non-destructive method has emerged as a powerful tool in cancer diagnostics, enabling discrimination of benign, malignant and metastatic tissues by detecting biochemical changes in nucleic acids, proteins, lipids and carbohydrates. Surface-enhanced Raman spectroscopy (SERS) further amplifies weak signals through metallic nanostructures, permitting detection of low-abundance biomarkers and subtle metabolic shifts. Raman imaging produces spatially resolved chemical maps across tissue sections, guiding histopathological assessment without the need for stains or exogenous labels. Integration of machine learning and multivariate analysis has accelerated spectral pattern recognition, improving diagnostic accuracy and enabling real-time decision-making during surgical procedures. Clinical applications now encompass in vivo delineation of brain tumour margins, ex vivo evaluation of biopsy cores, and quantitative assessment of receptor expression in breast cancer cells. Emerging approaches combine Raman “spectromics” with genomic or transcriptomic workflows to deliver high-dimensional molecular characterisation. As portable instrumentation and fibre-optic probes mature for patient-side deployment, Raman spectroscopy is poised to complement existing imaging modalities and inform precision oncology through rapid, objective insights into tumour heterogeneity and treatment response.

Research from Nature Portfolio

Recent work has focused on integrating high-wavenumber Raman spectroscopy into clinical workflows for targeted brain biopsy. A handheld, label-free system engineered within a standard needle platform enables real-time molecular analysis of tissue prior to harvesting, achieving sensitivity and specificity around 80–90% in detecting dense tumour regions. Validation in large-animal models confirms its ability to distinguish malignant from healthy brain tissue in situ, thereby mitigating sampling errors and reducing the need for repeat biopsies. These advances herald the clinical translation of Raman-guided optical molecular imaging for high-yield, safe neurosurgical interventions.

Raman Spectroscopy Applications in Cancer Diagnostics publication trend

The graph below shows the total number of articles in raman spectroscopy applications in cancer diagnostics across all publications each year (not limited to Nature Index journals).

Technical terms

Raman spectroscopy: An optical method that measures inelastically scattered light to generate molecular vibrational fingerprints of a sample.

Surface-enhanced Raman spectroscopy (SERS): A technique that uses metallic nanostructures to amplify Raman signals, boosting sensitivity to low-concentration analytes.

Raman imaging: A modality that collects spatially resolved Raman spectra across a sample, producing chemical maps without labels or stains.

Label-free: Describes methods that do not require external markers or dyes, relying solely on intrinsic molecular signals.

Multivariate analysis: Statistical approaches that process complex spectral datasets to identify patterns and classify tissue types.

References

1. Recent application of Raman spectroscopy in tumor diagnosis: from conventional methods to artificial intelligence fusion. PhotoniX (2023).
2. Translating genomic tools to Raman spectroscopy analysis enables high-dimensional tissue characterization on molecular resolution. Nature Communications (2023).
3. Combined SERS-Raman screening of HER2-overexpressing or silenced breast cancer cell lines. Journal of Nanobiotechnology (2024).
4. Applications of Raman spectroscopy in cancer diagnosis. Cancer and Metastasis Reviews (2018).
5. A new method using Raman spectroscopy for in vivo targeted brain cancer tissue biopsy. Scientific Reports (2018).