# Raman Spectroscopic Signatures of Hepatic Carcinoma: Progress and Future Prospect

## TL;DR

Raman spectroscopy is being explored as a non-invasive tool for detecting liver cancer by analyzing molecular changes in biological samples.

## Contribution

The paper reviews recent advances in Raman spectroscopy for liver cancer diagnosis and discusses future directions for clinical application.

## Key findings

- Raman spectroscopy can distinguish malignant from non-malignant liver conditions using biochemical signatures in biofluids and tissues.
- Machine learning and chemometric methods have enhanced the diagnostic accuracy of Raman spectroscopy for liver cancer detection.
- Complementary techniques like SERS and ROA are expanding the clinical potential of Raman-based diagnostics.

## Abstract

Liver cancer continues to be a predominant cause of cancer-related mortality globally, primarily attributable to late diagnosis and a scarcity of dependable biomarkers for early identification. Raman spectroscopy has emerged as a valuable analytical instrument for liver cancer detection, providing rapid, label-free, and non-destructive molecular profiling of biological specimens. Raman-based methodologies can discern malignant from non-malignant conditions by analyzing small biochemical alterations in biofluids, including blood, urine, and exosomes, as well as in liver tissue, yielding unique spectrum fingerprints. Progress in chemometric analysis, including machine learning models and multivariate statistical methods, has significantly improved the diagnostic precision of Raman spectroscopy, attaining elevated sensitivity and specificity across numerous studies. Furthermore, the integration of complementary techniques, such as surface-enhanced Raman spectroscopy (SERS) and Raman optical activity (ROA) has broadened its prospects for clinical application. This review article elucidates the contemporary applications of Raman spectroscopy in the diagnosis of liver cancer, presents pivotal findings across various sample types, and examines the challenges and future prospects of building Raman-based platforms as dependable diagnostic instruments in oncology.