RAMAN SPECTROSCOPY IN CANCER DIAGNOSTICS AND BIO-IMAGING: BRIDGING PRECISION MEDICINE AND ARTIFICIAL INTELLIGENCE

Abstract

Raman Spectroscopy (RS) has become a key diagnostic instrument for cancer detection along with bio-imaging as it offers non-contact sample analysis through label-less approaches which provides detailed chemical signatures in biological specimens. The fundamental aspects of RS and its versions like SurfaceEnhanced Raman Spectroscopy (SERS), Raman imaging (RI) and Tip-Enhanced Raman Spectroscopy (TERS) are emphasized for their functions in enhancing detection accuracy and resolution performance. RS finds clinical implementation throughout cancer types including lung tissue and breast tissue as well as thyroid tissue, liver tissue and colorectal tissue where it helps identify early conditions while the surgeon operates under real-time and provide accurate margin assessments for tumor removal. Bridging RS with advanced Machine Learning (ML) approaches using convolutional neural networks (CNNs) along with Raman Net models delivers better spectral identification performance while overcoming noise issues. The field of RS progressed from examination of tissues to study of individual cells which permits the examination of tumor variations and metastatic properties. Raman-based optical probes and hybrid systems allow researchers to use the technology for in vivo imaging while monitoring therapy using these systems. Integration of Artificial Intelligence (AI) and ML with Raman has proven beneficial in terms of fast tracking the results and better accuracy. This review reveals that RS keeps expanding its role in precise cancer care while promising diagnostic advancements as well as individualized medical treatments leading to better result outcomes.