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

Mukta Sharma; Ying-Chang Li; S N Manjunatha; Chia-Lung Tsai; Ray-Ming Lin; Shiang-Fu Huang; Liann-Be Chang

doi:10.3390/biomedicines11071984

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

Biomedicines. 2023 Jul 13;11(7):1984. doi: 10.3390/biomedicines11071984.

Authors

Mukta Sharma¹, Ying-Chang Li², S N Manjunatha¹, Chia-Lung Tsai^{1

3}, Ray-Ming Lin¹, Shiang-Fu Huang^{3

4}, Liann-Be Chang¹

Affiliations

¹ Department of Electronic Engineering, Chang Gung University, Taoyuan 33302, Taiwan.
² Department of Ph.D. Program, Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taichung 411030, Taiwan.
³ Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou 333, Taiwan.
⁴ Department of Public Health, Chang Gung University, Taoyuan 33302, Taiwan.

Abstract

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.

Keywords: Raman spectroscopy; oral cancer; partial least squares; support vector machine; tissue.

Grants and funding

Authors would like to thank Chang Gung University and Chang Gung Memorial Hospital for supporting this research financially (under Contract No. BMRP999) and Grants MOST 110-2221-E-182-056-MY2, 110-2622-E-182-007, and 108-2221-E-182-055 from the National Science and Technology Council (Taiwan).