Speaker
Description
Raman spectroscopy is a powerful, non-invasive method to analyze molecular structure through the inelastic scattering of photons. Inside the broader framework of coherent Raman techniques, spontaneous Raman remains as a clinically accessible and versatile approach. The intrinsic vibrational modes of molecular bonds allow the identification of biochemical differences between components; these spectral features act as optical biomarkers of biochemical and metabolic processes.
Thanks to advances in instrumentation, we explore Raman spectroscopy as a label-free diagnostic tool applied in neurosurgery, offering a new dimension of intraoperative feedback in neurosurgical procedures where tissue margins are often ambiguous. Particular attention is given to the spectral regions and band intensities most correlated with malignancy, as variations in lipid, protein, and nucleic acid content, those along with the use of statistical and machine learning techniques for automated tissue classification.
To overcome the inherent signal limitations of Raman scattering in biological samples, such as fluorescence background, baseline distortions, and inter-patient variability we employed a physics-informed machine learning approach. Spectral normalization was performed using quotient-based correction against independent reference signals to reduce instrumentation-induced multiplicative effects. A convolutional neural network, trained on simulated Raman data, was then used to denoise and enhance spectral quality while preserving fine chemical detail. These AI-assisted preprocessing steps enable robust extraction of tumor-specific optical signatures across heterogeneous brain tissue samples, paving the way for real-time, intraoperative classification in neurosurgical settings.
Raman analysis offers practical utility in the neurosurgical setting, particularly for delineating tumor boundaries and assisting in intraoperative decision-making. The ability to optically distinguish tumor from healthy parenchyma at a molecular level could improve the precision of surgical resections and may complement histopathological workflows and aid in real-time classification of brain tumor subtypes by capturing biochemical gradients across tumor margins might gain insights even into the metabolism of tumors.
By applying fundamental principles of atomic and molecular spectroscopy to a frontier biomedical challenge, have the translational potential of molecular optics not only to probe structure, but to understand and actively guide interventions actively supporting clinical and therapeutic strategies, highlighting the growing role of optical physics in medical contexts creating new pathways for interdisciplinary research at the intersection of photonics and biomedicine, converging in the development of accessible, hybrid clinical tools for multimodal optical diagnostics.