Identification of Healthy Tissue from Malignant Tissue in Surgical Margin Using Raman Spectroscopy in Oral Cancer Surgeries

• Published in: Biomedicines Vol. 11; no. 7; p. 1984
• Main Authors: Sharma, Mukta, Li, Ying-Chang, Manjunatha, S N, Tsai, Chia-Lung, Lin, Ray-Ming, Huang, Shiang-Fu, Chang, Liann-Be
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.07.2023; MDPI
• Subjects: Biopsy; Cancer surgery; Cryopreservation; Data analysis; Datasets; Discriminant analysis; Health aspects; Homeopathy; Materia medica and therapeutics; Medical prognosis; Medical research; Medicine, Experimental; Morphology; Nucleic acids; Oral cancer; Otolaryngology; partial least squares; Patients; Proteins; Raman spectroscopy; Spectrum analysis; Squamous cell carcinoma; support vector machine; Support vector machines; Therapeutics; tissue; Tumors; Taiwan; tissue; support vector machine; Raman spectroscopy; partial least squares; oral cancer
• ISSN: 2227-9059; 2227-9059
• DOI: 10.3390/biomedicines11071984

Accurate identification of tissue types in surgical margins is essential for ensuring the complete removal of cancerous cells and minimizing the risk of recurrence. The objective of this study was to explore the clinical utility of Raman spectroscopy for the detection of oral squamous cell carcinoma (OSCC) in both tumor and healthy tissues obtained from surgical resection specimens during surgery. This study enrolled a total of 64 patients diagnosed with OSCC. Among the participants, approximately 50% of the cases were classified as the most advanced stage, referred to as T4. Raman experiments were conducted on cryopreserved tissue samples collected from patients diagnosed with OSCC. Prominent spectral regions containing key oral biomarkers were analyzed using the partial least squares-support vector machine (PLS-SVM) method, which is a powerful multivariate analysis technique for discriminant analysis. This approach effectively differentiated OSCC tissue from non-OSCC tissue, achieving a sensitivity of 95.7% and a specificity of 93.3% with 94.7% accuracy. In the current study, Raman analysis of fresh tissue samples showed that OSCC tissues contained significantly higher levels of nucleic acids, proteins, and several amino acids compared to the adjacent healthy tissues. In addition to differentiating between OSCC and non-OSCC tissues, we have also explored the potential of Raman spectroscopy in classifying different stages of OSCC. Specifically, we have investigated the classification of T1, T2, T3, and T4 stages based on their Raman spectra. These findings emphasize the importance of considering both stage and subsite factors in the application of Raman spectroscopy for OSCC analysis. Future work will focus on expanding our tissue sample collection to better comprehend how different subsites influence the Raman spectra of OSCC at various stages, aiming to improve diagnostic accuracy and aid in identifying tumor-free margins during surgical interventions.