Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians’ Hands for In Situ Cancer Diagnosis and Surgery Guidance

Abstract

Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic “molecular finger-print” of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.

Discovering Glioma Tissue through Its Biomarkers’ Detection in Blood by Raman Spectroscopy and Machine Learning

“…We applied a non-linear regression model to select the most informative Raman spectra features associated with tumor growth (see Figure 9 and Table 3 ). As can be seen from Table 3 , the frequencies found by us are in good agreement with the Raman spectra of glioma tissues presented by other authors [ 44 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ]. It should be understood that the Raman spectrum of a sample is a superposition of vibrations of all substances present in the sample [ 15 ].…”

“…Informative Raman shifts are: 417, 420, 818, 1746, 2807, 3039, 3212, and 3563 cm −1 . Table 3 shows the correspondence between the Raman shifts for the constructed regression model, molecular vibrations, and the corresponding substances [ 44 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ].…”

Image-guided cancer surgery: a narrative review on imaging modalities and emerging nanotechnology strategies

“…Time resolution may have hampered the Raman imaging clinical implementation, and the technological advancement in this field is focused on enhancement of signal and speed acquisition. The entrance of machine learning and deep learning models in the data analysis has contributed to overcoming some of the method’s limits [ 41 ].…”

Advancements in Intraoperative Near-Infrared Fluorescence Imaging for Accurate Tumor Resection: A Promising Technique for Improved Surgical Outcomes and Patient Survival

“…Another limitation is strong background autofluorescence, which can interfere with the Raman signal. Additionally, potential tissue damage may occur due to laser heating during RS measurements . To overcome these limitations, researchers are exploring new strategies to improve RS, such as using enhanced Raman scattering techniques to improve sensitivity, developing new approaches for background subtraction and noise reduction, and using lower laser power to reduce tissue damage.…”