Issue 17, 2023

Intraoperative assessment of resection margins by Raman spectroscopy to guide oral cancer surgery

Abstract

Patients with oral cavity cancer are almost always treated with surgery. The goal is to remove the tumor with a margin of more than 5 mm of surrounding healthy tissue. Unfortunately, this is only achieved in about 15% to 26% of cases. Intraoperative assessment of tumor resection margins (IOARM) can dramatically improve surgical results. However, current methods are laborious, subjective, and logistically demanding. This hinders broad adoption of IOARM, to the detriment of patients. Here we present the development and validation of a high-wavenumber Raman spectroscopic technology, for quick and objective intraoperative measurement of resection margins on fresh specimens. It employs a thin fiber-optic needle probe, which is inserted into the tissue, to measure the distance between a resection surface and the tumor. A tissue classification model was developed to discriminate oral cavity squamous cell carcinoma (OCSCC) from healthy oral tissue, with a sensitivity of 0.85 and a specificity of 0.92. The tissue classification model was then used to develop a margin length prediction model, showing a mean difference between margin length predicted by Raman spectroscopy and histopathology of −0.17 mm.

Graphical abstract: Intraoperative assessment of resection margins by Raman spectroscopy to guide oral cancer surgery

Supplementary files

Article information

Article type
Paper
Submitted
25 apr 2023
Accepted
19 jul 2023
First published
19 jul 2023

Analyst, 2023,148, 4116-4126

Intraoperative assessment of resection margins by Raman spectroscopy to guide oral cancer surgery

Y. Aaboubout, M. R. Nunes Soares, T. C. Bakker Schut, E. M. Barroso, M. van der Wolf, E. Sokolova, V. Artyushenko, A. Bocharnikov, I. Usenov, C. G. F. van Lanschot, L. Ottevanger, H. Mast, I. ten Hove, B. P. Jonker, S. Keereweer, D. A. Monserez, A. Sewnaik, J. A. Hardillo, R. J. Baatenburg de Jong, S. Koljenović and G. J. Puppels, Analyst, 2023, 148, 4116 DOI: 10.1039/D3AN00650F

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