Evaluation of the diagnostic efficacy and spectrum of autofluorescence of benign, dysplastic and malignant lesions of the oral cavity using VELscope

Ganga, Ravikant S.; Gundre, Dipali; Bansal, Shivani; Shirsat, Pankaj M; Prasad, Pooja; Desai, Rajiv S.

doi:10.1016/j.oraloncology.2017.10.023

Cited by 47 publications

(39 citation statements)

References 39 publications

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“…Awan [19] demonstrated the relatively high sensitivity, 84% and a low specificity 15%, in differentiating high-risk lesions from benign lesions. Similarly, Ganga [26] obtained sensitivity and specificity values of 76% and 66.29%, respectively. They suggested that VELscope has reasonable sensitivity, but yields many false-positive results.…”

Section: Introductionmentioning

confidence: 91%

“…At these wavelengths, the normal oral mucosa is associated with a pale green fluorescence as viewed through a filter and abnormal tissue is associated with a loss of autofluorescence and appears dark. Many works [19][20][21][22][23][24][25][26] have evaluated the efficacy of the VELscope by direct comparing VELscope results with the biopsy reports. VELscope has been shown to have high sensitivity and to assist in the detection of oral lesions.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…In recent years, one such possible diagnosis method involving the application of autofluorescence imaging has been increasingly used. Currently, the VELscope® (LED Dental Inc., Vancouver, Canada; Awan, Morgan, & Warnakulasuriya, ; Ganga et al, ; Hanken et al, ; Huang et al, ; Yamamoto et al, ) and DIFOTI® (Electro‐Optical Sciences Inc., Irvington, NY, USA; Bin‐Shuwaish, Yaman, Dennison, & Neiva, ) systems have been launched for general dental clinics (Lingen, Kalmar, Karrison, & Speight, ). These instruments can detect a decrease or increase in the autofluorescence of flavin adenine dinucleotide (FAD), nicotinamide adenine dinucleotide (NADH), or collagen cross linking, by irradiating the lesional tissue with blue light of a wavelength of 400 to 460 nm (Laronde et al, ; Lingen et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…A number of studies on the autofluorescence imaging method have been concerned with validating the differential diagnosis of OSCC and OPMD, by making comparisons between the FVL of the suspected lesion and cytodiagnosis or biopsy (Awan et al, ; Elvers et al, ; Ganga et al, ; Petruzzi et al, ; Scheer et al, ; Yamamoto et al, ). However, there is currently no standardized numerical cutoff value for assisting in the identification of tumors using such autofluorescence visualization devices.…”

Section: Introductionmentioning

confidence: 99%

The luminance ratio of autofluorescence in a xenograft mouse model is stable through tumor growth stages

Sumi

Umemura

Adachi

et al. 2018

Clinical & Exp Dental Res

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The aim of this research was to investigate the value of autofluorescence imaging of oral cancer across different stages of tumor growth, to assist in detecting tumors. A xenograft mouse model was created with human oral squamous cell carcinoma cell line HSC‐3 being subcutaneously inoculated into nude mice. Tumor imaging was performed with an autofluorescence imaging method (Illumiscan®) using the luminance ratio, which was defined as the luminance of the tumor site over the luminance of normal skin tissue normalized to a value of 1.0. This luminance ratio was continuously observed postinoculation. Tumor and normal skin tissues were harvested, and differences in the concentrations of flavin adenine dinucleotide and nicotinamide adenine dinucleotide were examined. The luminance ratio of the tumor sites was 0.85 ± 0.05, and there was no significant change in the ratio over time, even if the tumor proliferated and expanded. Furthermore, flavin adenine dinucleotide and nicotinamide adenine dinucleotide were significantly lower in tumor tissue than in normal skin tissue. A luminance ratio under 0.90 indicates a high possibility of tumor, irrespective of the tumor growth stage. However, this cutoff value was determined using a xenograft mouse model and therefore requires further validation before being used in clinical diagnosis.

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“…Commercial devices exploiting skin multispectral reflectance and fluorescence features such as MelaFind, SIAscope, Velscope are suitable for non-invasive cancer diagnostics. However, their insufficient specificity is the main limiting factor for wide clinical implementation in routine clinical praxis [14,15]. Thanks to wide accessibility and rapid development of computing power and the integrated camera image quality, smartphone applications for skin cancer diagnostics is an emerging field of studies [16].…”

Section: Introductionmentioning

confidence: 99%

Differentiation of seborrheic keratosis from basal cell carcinoma, nevi and melanoma by RGB autofluorescence imaging

Lihachev

Lihacova

Plorina

et al. 2018

Biomed. Opt. Express

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A clinical trial on the autofluorescence imaging of skin lesions comprising 16 dermatologically confirmed pigmented nevi, 15 seborrheic keratosis, 2 dysplastic nevi, histologically confirmed 17 basal cell carcinomas and 1 melanoma was performed. The autofluorescence spatial properties of the skin lesions were acquired by smartphone RGB camera under 405 nm LED excitation. The diagnostic criterion is based on the calculation of the mean autofluorescence intensity of the examined lesion in the spectral range of 515 nm-700 nm. The proposed methodology is able to differentiate seborrheic keratosis from basal cell carcinoma, pigmented nevi and melanoma. The sensitivity and specificity of the proposed method was estimated as being close to 100%. The proposed methodology and potential clinical applications are discussed in this article.

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Evaluation of the diagnostic efficacy and spectrum of autofluorescence of benign, dysplastic and malignant lesions of the oral cavity using VELscope

Cited by 47 publications

References 39 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

The luminance ratio of autofluorescence in a xenograft mouse model is stable through tumor growth stages

Differentiation of seborrheic keratosis from basal cell carcinoma, nevi and melanoma by RGB autofluorescence imaging

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