Diagnosis of oral cancer by light-induced autofluorescence spectroscopy using double excitation wavelengths

Wang, Chih Yu; Chiang, Huihua Kenny; Chen, Chin-Tin; Chiang, Chun‐Pin; Kuo, Ying-Shiung; Chow, Song Nan

doi:10.1016/s1368-8375(98)00107-9

Cited by 53 publications

(31 citation statements)

References 20 publications

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“…[27][28][29][30][31][32][33][34][35][36] Challenges are similar to those cited in the previous paragraph. Additionally, the abundance of data generated and our lack of complete understanding of the carcinogenesis process renders data analysis and interpretation very challenging.…”

Section: Endogenous Fluorescencesupporting

confidence: 56%

Noninvasive imaging of oral premalignancy and malignancy

et al. 2005

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Abstract. Early detection of cancer and its precursors remains the best way to ensure patient survival and quality of life. Our specific aim is to test a multimodality approach to noninvasive diagnostics of oral premalignancy and malignancy. In the hamster cheek pouch model ͑120 hamsters͒, in vivo optical coherence tomography ͑OCT͒ and optical Doppler tomography ͑ODT͒ map epithelial, subepithelial, and vascular change throughout carcinogenesis. In vivo multiwavelength multiphoton ͑MPM͒ and second-harmonic generated ͑SHG͒ fluorescence techniques provided parallel data on surface and subsurface tissue structure, specifically collagen presence and structure, cellular presence, and vasculature. Images are diagnosed by two blinded, prestandardized investigators using a scale from 0 to 6 for all modalities. After sacrifice, histopathology is evaluated on a scale of 0 to 6. Imaging data are reproducibly obtained with good accuracy. Carcinogenesis-related structural and vascular changes are clearly visible to tissue depths of 2 mm. Sensitivity ͑OCT/ODT alone, 71 to 88%; OCT+ MPM/ SHG, 79 to 91%͒ and specificity ͑OCT alone, 62 to 83%; OCT+ MPM/ SHG, 67 to 90%͒ compare well with conventional techniques. Our conclusions are that OCT/ODT and MPM/SHG are promising noninvasive in vivo diagnostic modalities for oral dysplasia and malignancy.

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Section: Endogenous Fluorescencesupporting

confidence: 56%

Noninvasive imaging of oral premalignancy and malignancy

et al. 2005

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“…To our surprise, this contradicts the results found in our pilot study by Van Staveren et al 17 Our results for distinguishing lesions from healthy oral mucosa, as well as for distinguishing cancer from healthy mucosa, were comparable with results found in the literature. Majumder et al 11 achieved 86% sensitivity, 63% specificity for distinguishing oral cancer from healthy mucosa, while Wang et al 12 achieved a sensitivity of 81% and a specificity of 94% for the same problem. Gillenwater et al 9 established a 94% sensitivity, 100% specificity for distinguishing abnormal from normal oral mucosa, while Müller et al 20 found a 96% sensitivity, 96% specificity for the same question when using a combination of autofluorescence and diffuse reflectance measurements.…”

Section: Discussionmentioning

confidence: 99%

“…4,8,9,12,19,[23][24][25][26][27][28][29] All methods were performed on the basis of leave-one-out classification, to ensure that no overly positive results were obtained. We investigated all six excitation wavelengths.…”

Section: Classification Of (Pre-)malignant Versus Benign Lesionsmentioning

confidence: 99%

“…[7][8][9][10][11][12] In a previous pilot study for the staging of oral leukoplakia performed by our group we obtained a sensitivity of 86% and a specificity of 100% for distinguishing abnormal from normal tissue. However, the distinction between lesions in general and healthy oral mucosa is not relevant for the clinical question of staging visible lesions and finding the optimal location for biopsy, since the presence of a lesion is already established by its being noticed.…”

Section: Introductionmentioning

confidence: 99%

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Clinical study for classification of benign, dysplastic, and malignant oral lesions using autofluorescence spectroscopy

et al. 2004

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Abstract. Autofluorescence spectroscopy shows promising results for detection and staging of oral (pre-)malignancies. To improve staging reliability, we develop and compare algorithms for lesion classification. Furthermore, we examine the potential for detecting invisible tissue alterations. Autofluorescence spectra are recorded at six excitation wavelengths from 172 benign, dysplastic, and cancerous lesions and from 97 healthy volunteers. We apply principal components analysis (PCA), artificial neural networks, and red/green intensity ratio's to separate benign from (pre-)malignant lesions, using four normalization techniques. To assess the potential for detecting invisible tissue alterations, we compare PC scores of healthy mucosa and surroundings/contralateral positions of lesions. The spectra show large variations in shape and intensity within each lesion group. Intensities and PC score distributions demonstrate large overlap between benign and (pre-)malignant lesions. The receiver-operator characteristic areas under the curve (ROC-AUCs) for distinguishing cancerous from healthy tissue are excellent (0.90 to 0.97).

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“…28 Therefore, in this study, cryomatrix was used to embed tissue samples and cryosections were prepared for Raman measurements. In addition, CaF 2 slides were used as a substrate of the tissue sections for all the Raman measurement to avoid background signal.…”

Section: Confocal Raman Microspectroscopymentioning

confidence: 99%

Recognizing different tissues in human fetal femur cartilage by label-free Raman microspectroscopy

et al. 2012

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Abstract. Traditionally, the composition of bone and cartilage is determined by standard histological methods. We used Raman microscopy, which provides a molecular "fingerprint" of the investigated sample, to detect differences between the zones in human fetal femur cartilage without the need for additional staining or labeling. Raman area scans were made from the (pre)articular cartilage, resting, proliferative, and hypertrophic zones of growth plate and endochondral bone within human fetal femora. Multivariate data analysis was performed on Raman spectral datasets to construct cluster images with corresponding cluster averages. Cluster analysis resulted in detection of individual chondrocyte spectra that could be separated from cartilage extracellular matrix (ECM) spectra and was verified by comparing cluster images with intensity-based Raman images for the deoxyribonucleic acid/ ribonucleic acid (DNA/RNA) band. Specific dendrograms were created using Ward's clustering method, and principal component analysis (PCA) was performed with the separated and averaged Raman spectra of cells and ECM of all measured zones. Overall (dis)similarities between measured zones were effectively visualized on the dendrograms and main spectral differences were revealed by PCA allowing for label-free detection of individual cartilaginous zones and for label-free evaluation of proper cartilaginous matrix formation for future tissue engineering and clinical purposes.

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Diagnosis of oral cancer by light-induced autofluorescence spectroscopy using double excitation wavelengths

Cited by 53 publications

References 20 publications

Noninvasive imaging of oral premalignancy and malignancy

Noninvasive imaging of oral premalignancy and malignancy

Clinical study for classification of benign, dysplastic, and malignant oral lesions using autofluorescence spectroscopy

Recognizing different tissues in human fetal femur cartilage by label-free Raman microspectroscopy

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