Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection

Jeng, Ming–Jer; Sharma, Mukta; Sharma, Lokesh; Chao, Ting-Yu; Huang, Shiang‐Fu; Chang, Liann‐Be; Wu, Shih‐Lin; Chow, Lee

doi:10.3390/jcm8091313

Cited by 71 publications

(59 citation statements)

References 41 publications

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“…LDA yields a linear boundary whereas QDA produces a quadratic boundary of the classifier, resulting in substantially greater variance because it is quadratic [30]. Both classifiers are very helpful in medical applications [31,32]. Therefore, the effectiveness of both in distinguishing among normal, premalignant and malignant lesions is studied herein.…”

Section: Discussionmentioning

confidence: 99%

Multiclass classification of autofluorescence images of oral cavity lesions based on quantitative analysis

et al. 2020

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Background Oral cancer is one of the most common diseases globally. Conventional oral examination and histopathological examination are the two main clinical methods for diagnosing oral cancer early. VELscope is an oral cancer-screening device that exploited autofluorescence. It yields inconsistent results when used to differentiate between normal, premalignant and malignant lesions. We develop a new method to increase the accuracy of differentiation. Materials and methods Five samples (images) of each of 21 normal mucosae, as well as 31 premalignant and 16 malignant lesions of the tongue and buccal mucosa were collected under both white light and autofluorescence (VELscope, 400-460 nm wavelength). The images were developed using an iPod (Apple, Atlanta Georgia, USA). Results The normalized intensity and standard deviation of intensity were calculated to classify image pixels from the region of interest (ROI). Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) classifiers were used. The performance of both of the classifiers was evaluated with respect to accuracy, precision, and recall. These parameters were used for multiclass classification. The accuracy rate of LDA with un-normalized data was increased by 2% and 14% and that of QDA was increased by 16% and 25% for the tongue and buccal mucosa, respectively. Conclusion The QDA algorithm outperforms the LDA classifier in the analysis of autofluorescence images with respect to all of the standard evaluation parameters.

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Section: Discussionmentioning

confidence: 99%

Multiclass classification of autofluorescence images of oral cavity lesions based on quantitative analysis

et al. 2020

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“…Raman spectroscopy probes the unique vibrational fingerprint of molecules assisting in analyte identification. Using Raman spectra, the differences in protein, amino acid, and beta carotene can be determined, which are excellent biomolecular differences markers for cancer detection [ 104 ]. Fourier transform infrared (FTIR) spectroscopy associated with computational systems has been used to distinguish between benign and malignant tumors accurately [ 105 ].…”

Section: Techniques Used For Diagnosis Of Oral Cancermentioning

confidence: 99%

Oral cancer diagnosis and perspectives in India

Borse

Konwar

Buragohain

2020

Sensors International

222

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Globally, oral cancer is the sixth most common type of cancer with India contributing to almost one-third of the total burden and the second country having the highest number of oral cancer cases. Oral squamous cell carcinoma (OSCC) dominates all the oral cancer cases with potentially malignant disorders, which is also recognized as a detectable pre-clinical phase of oral cancer. Tobacco consumption including smokeless tobacco, betel-quid chewing, excessive alcohol consumption, unhygienic oral condition, and sustained viral infections that include the human papillomavirus are some of the risk aspects for the incidence of oral cancer. Lack of knowledge, variations in exposure to the environment, and behavioral risk factors indicate a wide variation in the global incidence and increases the mortality rate. This review describes various risk factors related to the occurrence of oral cancer, the statistics of the distribution of oral cancer in India by various virtues, and the socio-economic positions. The various conventional diagnostic techniques used routinely for detection of the oral cancer are discussed along with advanced techniques. This review also focusses on the novel techniques developed by Indian researchers that have huge potential for application in oral cancer diagnosis.

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“…Therefore, ALA-PDD in combination with OCT and pCLE might clarify alternatives of cell metabolism and the tissue structure of oral mucosa lesions, thereby improving the diagnostic accuracy. Contrarily, RS can highlight the characterization of molecules and their alternatives in cells and tissues by analyzing Raman spectrum, consisting of scattered light of various wavelengths from molecules following irradiation [47]. Thus, RS might identify a slightly different PpIX concentration between normal and malignant cells.…”

Section: Issues and Future Perspectivesmentioning

confidence: 99%

Non-Invasive Diagnostic System Based on Light for Detecting Early-Stage Oral Cancer and High-Risk Precancerous Lesions—Potential for Dentistry

Tatehara

Satomura

2020

Cancers

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Oral health promotion and examinations have contributed to the early detection of oral cancer and oral potentially malignant disorders, leading to the adaptation of minimally invasive therapies and subsequent improvements in the prognosis/maintenance of the quality of life after treatments. However, the accurate detection of early-stage oral cancer and oral epithelial dysplasia is particularly difficult for conventional oral examinations because these lesions sometimes resemble benign lesions or healthy oral mucosa tissues. Although oral biopsy has been considered the gold standard for accurate diagnosis, it is deemed invasive for patients. For this reason, most clinicians are looking forward to the development of non-invasive diagnostic technologies to detect and distinguish between cancerous and benign lesions. To date, several non-invasive adjunctive fluorescence-based detection systems have improved the accuracy of the detection and diagnosis of oral mucosal lesions. Autofluorescence-based systems can detect lesions as a loss of autofluorescence through irradiation with blue-violet lights. Photodynamic diagnosis using 5-aminolevulinic acid (ALA-PDD) shows the presence of very early oral cancers and oral epithelial dysplasia as a red fluorescent area. In this article, currently used fluorescence-based diagnostic methods are introduced and discussed from a clinical point of view.

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Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection

Cited by 71 publications

References 41 publications

Multiclass classification of autofluorescence images of oral cavity lesions based on quantitative analysis

Multiclass classification of autofluorescence images of oral cavity lesions based on quantitative analysis

Oral cancer diagnosis and perspectives in India

Non-Invasive Diagnostic System Based on Light for Detecting Early-Stage Oral Cancer and High-Risk Precancerous Lesions—Potential for Dentistry

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