Developing an Algorithm for Discriminating Oral Cancerous and Normal Tissues Using Raman Spectroscopy

The aim of this study was to investigate the clinical potential of Raman spectroscopy (RS) in detecting oral squamous cell carcinoma (OSCC) in tumor and healthy tissues in surgical resection specimens during surgery. Raman experiments were performed on cryopreserved specimens from patients with OSCC...

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Autores principales:	Mukta Sharma, Ming-Jer Jeng, Chi-Kuang Young, Shiang-Fu Huang, Liann-Be Chang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	oral cancer Raman spectroscopy PCA-LDA PLS-LDA cryopreserved tissue Medicine R
Acceso en línea:	https://doaj.org/article/4fa4cb3a7a914e8aa17570fcbe5373a7
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spelling	oai:doaj.org-article:4fa4cb3a7a914e8aa17570fcbe5373a72021-11-25T18:07:40ZDeveloping an Algorithm for Discriminating Oral Cancerous and Normal Tissues Using Raman Spectroscopy10.3390/jpm111111652075-4426https://doaj.org/article/4fa4cb3a7a914e8aa17570fcbe5373a72021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4426/11/11/1165https://doaj.org/toc/2075-4426The aim of this study was to investigate the clinical potential of Raman spectroscopy (RS) in detecting oral squamous cell carcinoma (OSCC) in tumor and healthy tissues in surgical resection specimens during surgery. Raman experiments were performed on cryopreserved specimens from patients with OSCC. Univariate and multivariate analysis was performed based on the fingerprint region (700–1800 cm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>) of the Raman spectra. One hundred thirty-one ex-vivo Raman experiments were performed on 131 surgical resection specimens obtained from 67 patients. The principal component analysis (PCA) and partial least square (PLS) methods with linear discriminant analysis (LDA) were applied on an independent validation dataset. Both models were able to differentiate between the tissue types, but PLS–LDA showed 100% accuracy, sensitivity, and specificity. In this study, Raman measurements of fresh resection tissue specimens demonstrated that OSCC had significantly higher nucleic acid, protein, and several amino acid contents than adjacent healthy tissues. The specific spectral information obtained in this study can be used to develop an in vivo Raman spectroscopic method for the tumor-free resection boundary during surgery.Mukta SharmaMing-Jer JengChi-Kuang YoungShiang-Fu HuangLiann-Be ChangMDPI AGarticleoral cancerRaman spectroscopyPCA-LDAPLS-LDAcryopreserved tissueMedicineRENJournal of Personalized Medicine, Vol 11, Iss 1165, p 1165 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	oral cancer Raman spectroscopy PCA-LDA PLS-LDA cryopreserved tissue Medicine R
spellingShingle	oral cancer Raman spectroscopy PCA-LDA PLS-LDA cryopreserved tissue Medicine R Mukta Sharma Ming-Jer Jeng Chi-Kuang Young Shiang-Fu Huang Liann-Be Chang Developing an Algorithm for Discriminating Oral Cancerous and Normal Tissues Using Raman Spectroscopy
description	The aim of this study was to investigate the clinical potential of Raman spectroscopy (RS) in detecting oral squamous cell carcinoma (OSCC) in tumor and healthy tissues in surgical resection specimens during surgery. Raman experiments were performed on cryopreserved specimens from patients with OSCC. Univariate and multivariate analysis was performed based on the fingerprint region (700–1800 cm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>) of the Raman spectra. One hundred thirty-one ex-vivo Raman experiments were performed on 131 surgical resection specimens obtained from 67 patients. The principal component analysis (PCA) and partial least square (PLS) methods with linear discriminant analysis (LDA) were applied on an independent validation dataset. Both models were able to differentiate between the tissue types, but PLS–LDA showed 100% accuracy, sensitivity, and specificity. In this study, Raman measurements of fresh resection tissue specimens demonstrated that OSCC had significantly higher nucleic acid, protein, and several amino acid contents than adjacent healthy tissues. The specific spectral information obtained in this study can be used to develop an in vivo Raman spectroscopic method for the tumor-free resection boundary during surgery.
format	article
author	Mukta Sharma Ming-Jer Jeng Chi-Kuang Young Shiang-Fu Huang Liann-Be Chang
author_facet	Mukta Sharma Ming-Jer Jeng Chi-Kuang Young Shiang-Fu Huang Liann-Be Chang
author_sort	Mukta Sharma
title	Developing an Algorithm for Discriminating Oral Cancerous and Normal Tissues Using Raman Spectroscopy
title_short	Developing an Algorithm for Discriminating Oral Cancerous and Normal Tissues Using Raman Spectroscopy
title_full	Developing an Algorithm for Discriminating Oral Cancerous and Normal Tissues Using Raman Spectroscopy
title_fullStr	Developing an Algorithm for Discriminating Oral Cancerous and Normal Tissues Using Raman Spectroscopy
title_full_unstemmed	Developing an Algorithm for Discriminating Oral Cancerous and Normal Tissues Using Raman Spectroscopy
title_sort	developing an algorithm for discriminating oral cancerous and normal tissues using raman spectroscopy
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4fa4cb3a7a914e8aa17570fcbe5373a7
work_keys_str_mv	AT muktasharma developinganalgorithmfordiscriminatingoralcancerousandnormaltissuesusingramanspectroscopy AT mingjerjeng developinganalgorithmfordiscriminatingoralcancerousandnormaltissuesusingramanspectroscopy AT chikuangyoung developinganalgorithmfordiscriminatingoralcancerousandnormaltissuesusingramanspectroscopy AT shiangfuhuang developinganalgorithmfordiscriminatingoralcancerousandnormaltissuesusingramanspectroscopy AT liannbechang developinganalgorithmfordiscriminatingoralcancerousandnormaltissuesusingramanspectroscopy
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Developing an Algorithm for Discriminating Oral Cancerous and Normal Tissues Using Raman Spectroscopy

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