Oral Cancer Discrimination and Novel Oral Epithelial Dysplasia Stratification Using FTIR Imaging and Machine Learning

The Fourier transform infrared (FTIR) imaging technique was used in a transmission model for the evaluation of twelve oral hyperkeratosis (HK), eleven oral epithelial dysplasia (OED), and eleven oral squamous cell carcinoma (OSCC) biopsy samples in the fingerprint region of 1800–950 cm<sup>−1&...

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Autores principales:	Rong Wang, Aparna Naidu, Yong Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Fourier transform infrared spectroscopy FTIR imaging spectral biomarker multivariate analysis machine learning discriminant model Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/626de092d2744eb58b42e1c9077b049a
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spelling	oai:doaj.org-article:626de092d2744eb58b42e1c9077b049a2021-11-25T17:21:54ZOral Cancer Discrimination and Novel Oral Epithelial Dysplasia Stratification Using FTIR Imaging and Machine Learning10.3390/diagnostics111121332075-4418https://doaj.org/article/626de092d2744eb58b42e1c9077b049a2021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/2133https://doaj.org/toc/2075-4418The Fourier transform infrared (FTIR) imaging technique was used in a transmission model for the evaluation of twelve oral hyperkeratosis (HK), eleven oral epithelial dysplasia (OED), and eleven oral squamous cell carcinoma (OSCC) biopsy samples in the fingerprint region of 1800–950 cm<sup>−1</sup>. A series of 100 µm × 100 µm FTIR imaging areas were defined in each sample section in reference to the hematoxylin and eosin staining image of an adjacent section of the same sample. After outlier removal, signal preprocessing, and cluster analysis, a representative spectrum was generated for only the epithelial tissue in each area. Two representative spectra were selected from each sample to reflect intra-sample heterogeneity, which resulted in a total of 68 representative spectra from 34 samples for further analysis. Exploratory analyses using Principal component analysis and hierarchical cluster analysis showed good separation between the HK and OSCC spectra and overlaps of OED spectra with either HK or OSCC spectra. Three machine learning discriminant models based on partial least squares discriminant analysis (PLSDA), support vector machines discriminant analysis (SVMDA), and extreme gradient boosting discriminant analysis (XGBDA) were trained using 46 representative spectra from 12 HK and 11 OSCC samples. The PLSDA model achieved 100% sensitivity and 100% specificity, while both SVM and XGBDA models generated 95% sensitivity and 96% specificity, respectively. The PLSDA discriminant model was further used to classify the 11 OED samples into HK-grade (6), OSCC-grade (4), or borderline case (1) based on their FTIR spectral similarity to either HK or OSCC cases, providing a potential risk stratification strategy for the precancerous OED samples. The results of the current study support the application of the FTIR-machine learning technique in early oral cancer detection.Rong WangAparna NaiduYong WangMDPI AGarticleFourier transform infrared spectroscopyFTIR imagingspectral biomarkermultivariate analysismachine learningdiscriminant modelMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 2133, p 2133 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Fourier transform infrared spectroscopy FTIR imaging spectral biomarker multivariate analysis machine learning discriminant model Medicine (General) R5-920
spellingShingle	Fourier transform infrared spectroscopy FTIR imaging spectral biomarker multivariate analysis machine learning discriminant model Medicine (General) R5-920 Rong Wang Aparna Naidu Yong Wang Oral Cancer Discrimination and Novel Oral Epithelial Dysplasia Stratification Using FTIR Imaging and Machine Learning
description	The Fourier transform infrared (FTIR) imaging technique was used in a transmission model for the evaluation of twelve oral hyperkeratosis (HK), eleven oral epithelial dysplasia (OED), and eleven oral squamous cell carcinoma (OSCC) biopsy samples in the fingerprint region of 1800–950 cm<sup>−1</sup>. A series of 100 µm × 100 µm FTIR imaging areas were defined in each sample section in reference to the hematoxylin and eosin staining image of an adjacent section of the same sample. After outlier removal, signal preprocessing, and cluster analysis, a representative spectrum was generated for only the epithelial tissue in each area. Two representative spectra were selected from each sample to reflect intra-sample heterogeneity, which resulted in a total of 68 representative spectra from 34 samples for further analysis. Exploratory analyses using Principal component analysis and hierarchical cluster analysis showed good separation between the HK and OSCC spectra and overlaps of OED spectra with either HK or OSCC spectra. Three machine learning discriminant models based on partial least squares discriminant analysis (PLSDA), support vector machines discriminant analysis (SVMDA), and extreme gradient boosting discriminant analysis (XGBDA) were trained using 46 representative spectra from 12 HK and 11 OSCC samples. The PLSDA model achieved 100% sensitivity and 100% specificity, while both SVM and XGBDA models generated 95% sensitivity and 96% specificity, respectively. The PLSDA discriminant model was further used to classify the 11 OED samples into HK-grade (6), OSCC-grade (4), or borderline case (1) based on their FTIR spectral similarity to either HK or OSCC cases, providing a potential risk stratification strategy for the precancerous OED samples. The results of the current study support the application of the FTIR-machine learning technique in early oral cancer detection.
format	article
author	Rong Wang Aparna Naidu Yong Wang
author_facet	Rong Wang Aparna Naidu Yong Wang
author_sort	Rong Wang
title	Oral Cancer Discrimination and Novel Oral Epithelial Dysplasia Stratification Using FTIR Imaging and Machine Learning
title_short	Oral Cancer Discrimination and Novel Oral Epithelial Dysplasia Stratification Using FTIR Imaging and Machine Learning
title_full	Oral Cancer Discrimination and Novel Oral Epithelial Dysplasia Stratification Using FTIR Imaging and Machine Learning
title_fullStr	Oral Cancer Discrimination and Novel Oral Epithelial Dysplasia Stratification Using FTIR Imaging and Machine Learning
title_full_unstemmed	Oral Cancer Discrimination and Novel Oral Epithelial Dysplasia Stratification Using FTIR Imaging and Machine Learning
title_sort	oral cancer discrimination and novel oral epithelial dysplasia stratification using ftir imaging and machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/626de092d2744eb58b42e1c9077b049a
work_keys_str_mv	AT rongwang oralcancerdiscriminationandnoveloralepithelialdysplasiastratificationusingftirimagingandmachinelearning AT aparnanaidu oralcancerdiscriminationandnoveloralepithelialdysplasiastratificationusingftirimagingandmachinelearning AT yongwang oralcancerdiscriminationandnoveloralepithelialdysplasiastratificationusingftirimagingandmachinelearning
_version_	1718412474026295296

Oral Cancer Discrimination and Novel Oral Epithelial Dysplasia Stratification Using FTIR Imaging and Machine Learning

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