多年來，各國團隊在生物自體螢光的研究發現，各種腫瘤和正常組織的自體螢光具有不同程度的變化，且此方法為非侵入式，只需蒐集影像或訊號，並不會觸碰病灶造成患者不適。因此，利用光譜儀技術取得螢光光譜特性、或是利用影像技術呈現螢光影像，用來觀測螢光的變化量，作為醫生診斷的輔助，一直是許多研究團隊的目標。
自體螢光在腫瘤及正常組織下主要差異有：(1) 螢光強度 (2) 代謝指標：腫瘤周邊血管增生，造成附近的組織缺氧及充血，由於血液的吸收係數在自體螢光的波段(400nm-600nm)較高，固激發出的自體螢光會被血液吸收，造成附近螢光強度減弱。本研究針對體內主要之自體螢光代謝物質：還原態菸鹼醯胺腺嘌呤雙核苷酸 (NADH) 以及黃素腺嘌呤二核苷酸 (FAD)，已有研究指出，在生物體內缺氧或發生腫瘤的情況，細胞內NADH的濃度會上升且FAD的濃度會下降。因此我們由NADH / (NADH + FAD) 稱之為Redox ratio，將其視為代謝指標，代表細胞的代謝狀況。
先前我們已做完一系列之動物實驗，發現在腫瘤/正常組織在螢光強度及Redox ratio之異質性(heterogeneity)有明顯的差異，因此我們將動物實驗之儀器微小化，使其能在人體臨床實驗使用。本儀器搭配CCD相機、激發自體螢光之LED(375nm/460)及其搭配之濾光片(479nm/525nm)，按下拍照鍵時會先拍攝460nm LED激發之螢光影像，自動切換濾鏡(479nm)及LED(375nm)後拍攝375nm LED激發之螢光影像，兩張螢光影像之拍攝位置為同一部位。
研究對象分為三類，(1)口腔癌之患者(確診)、(2)口腔有疑似病灶情形如：白斑(leukoplakia)、非典型增生(dysplasia/atypical hyperplasia)之患者，及(3)口腔健康之受試者，實驗者於成大醫院牙醫門診挑選，經過受試者本人同意後，用口腔螢光偵測儀拍攝其病灶及口腔各部位之螢光影像，用以分析口腔中之正常/不正常之特徵。
每張螢光影像會手動選擇病灶之ROI(region of interest)以避免牙齒及其他部位產生之螢光干擾，選擇ROI後，計算ROI中之螢光、Redox ratio之強度及異質性，並且利用二次分類器(QDA，Quadratic discriminant analysis)來區分正常/癌症，及正常/不正常(包括癌症及白斑症)之資料，結果顯示使用QDA區分正常/癌症時，有不錯的sensitivity 和 specificity。

In recent years, many research teams of autofluorescence had revealed that the autofluorescence intensity of tumor region is different from normal region, and some research results can be used in oral screening as well. This method is non-invasive, and it only needs to get the autofluorescence image/signal of tissue without touching it, thus so patient will not feel any pain. Therefore, helping doctors effectively discover cancerous cells by using fluorescence spectroscopy to obtain spectral characteristics, or using imaging technology to observe the changing intensity of fluorescence imaging is target of many research teams.
Autofluorescence is generated by tissue matrix or fluorophores in living cells. During the period of a tumor developing, the structure of tissue and metabolism will be changed. In this study, we are concerned with two fluorescent metabolites, Nicotinamide adenine dinucleotide (NADH) and Flavin adenine dinucleotide (FAD), and its Redox ratio NADH / (NADH +FAD) as our biomarker to indicate the metabolism of tissues. We developed a device to observe the autofluorescence; the device was installed with the LEDs as light source to excite NADH/FAD autofluorescence and one CCD camera covered by band-pass filters to separate the autofluorescence from excitation light.

To prove the device can be applied in cancer detection, we have done an animal trial. We injected cancer cell line into the right buccal mucosa of the hamsters and used the device to observe the tumor growing for a period of time. The result of this trial reveals that redox ratio and its heterogeneity is significant in distinguishing the tumor/normal cell. For this reason, we make the device of animal trial smaller to using in human clinical trial which has two wavelengths of LEDs as an exaction light source, and one CCD camera with two filters to get the suitable autofluorescence.
 
In this study, we use our device to get clinical data from the Dentistry of National Cheng Kung University Hospital which include the biopsy confirmed oral cancer, squamous cell carcinoma (SCC), and leukoplakia as the experimental group, some healthy people are chosen as the control group. We analyze the intensity and the heterogeneity of autofluorescence image and its redox ratio, and use quadratic discriminant analysis (QDA) classifier to classify data. The result shows that it has a good sensitivity and specificity in cancer detection.

1. Su, C.-C., et al., Distinctive features of oral cancer in Changhua County: high incidence, buccal mucosa preponderance, and a close relation to betel quid chewing habit. Journal of the Formosan Medical Association, 2007. 106(3): p. 225-233.
2. Ko, Y.C., et al., Betel quid chewing, cigarette smoking and alcohol consumption related to oral cancer in Taiwan. Journal of oral pathology & medicine, 1995. 24(10): p. 450-453.
3. Wagnieres, G.A., W.M. Star, and B.C. Wilson, In vivo fluorescence spectroscopy and imaging for oncological applications. Photochemistry and photobiology, 1998. 68(5): p. 603-632.
4. Poate, T., et al., An audit of the efficacy of the oral brush biopsy technique in a specialist Oral Medicine unit. Oral oncology, 2004. 40(8): p. 829-834.
5. Pavlova, I., et al., Understanding the biological basis of autofluorescence imaging for oral cancer detection: high-resolution fluorescence microscopy in viable tissue. Clinical Cancer Research, 2008. 14(8): p. 2396-2404.
6. Betz, C., et al., Autofluorescence imaging and spectroscopy of normal and malignant mucosa in patients with head and neck cancer. Lasers in surgery and medicine, 1999. 25(4): p. 323-334.
7. Roblyer, D., et al., Objective detection and delineation of oral neoplasia using autofluorescence imaging. Cancer Prevention Research, 2009. 2(5): p. 423-431.
8. Svistun, E., et al., Vision enhancement system for detection of oral cavity neoplasia based on autofluorescence. Head & neck, 2004. 26(3): p. 205-215.
9. Lane, P.M., et al., Simple device for the direct visualization of oral-cavity tissue fluorescence. Journal of biomedical optics, 2006. 11(2): p. 024006-024006-7.
10. Epstein, J., et al., Analysis of oral lesion biopsies identified and evaluated by visual examination, chemiluminescence and toluidine blue. Oral Oncology, 2008. 44(6): p. 538-544.
11. Stephen Barrett, M.D. ViziLite Screening: Does It Make Sense? ; Available from: http://www.dentalwatch.org/questionable/vizilite/overview.html.
12. Lingen, M.W., et al., Critical evaluation of diagnostic aids for the detection of oral cancer. Oral oncology, 2008. 44(1): p. 10-22.
13. Kollias, N., G. Zonios, and G.N. Stamatas, Fluorescence spectroscopy of skin. Vibrational Spectroscopy, 2002. 28(1): p. 17-23.
14. Vander Heiden, M.G., L.C. Cantley, and C.B. Thompson, Understanding the Warburg effect: the metabolic requirements of cell proliferation. science, 2009. 324(5930): p. 1029-1033.
15. Hsu, P.P. and D.M. Sabatini, Cancer cell metabolism: Warburg and beyond. Cell, 2008. 134(5): p. 703-707.
16. Ostrander, J.H., et al., Optical redox ratio differentiates breast cancer cell lines based on estrogen receptor status. Cancer research, 2010. 70(11): p. 4759-4766.
17. Drezek, R., et al., Understanding the contributions of NADH and collagen to cervical tissue fluorescence spectra: modeling, measurements, and implications. Journal of biomedical optics, 2001. 6(4): p. 385-396.
18. Li, L.Z., et al., Quantitative magnetic resonance and optical imaging biomarkers of melanoma metastatic potential. Proceedings of the National Academy of Sciences, 2009. 106(16): p. 6608-6613.
19. Xu, H.N., et al., Quantitative mitochondrial redox imaging of breast cancer metastatic potential. Journal of biomedical optics, 2010. 15(3): p. 036010-036010-10.
20. De Veld, D., et al., The status of in vivo autofluorescence spectroscopy and imaging for oral oncology. Oral oncology, 2005. 41(2): p. 117-131.
21. Sun, Y., et al., Fluorescence lifetime imaging microscopy:< i> in vivo</i> application to diagnosis of oral carcinoma. Optics letters, 2009. 34(13): p. 2081-2083.
22. Farah, C.S., et al., Efficacy of tissue autofluorescence imaging (VELScope) in the visualization of oral mucosal lesions. Head & neck, 2012. 34(6): p. 856-862.
23. Farah, C. and M. McCullough, Oral cancer awareness for the general practitioner: new approaches to patient care. Australian dental journal, 2008. 53(1): p. 2-10.
24. Liu, Q., et al., Compact point-detection fluorescence spectroscopy system for quantifying intrinsic fluorescence redox ratio in brain cancer diagnostics. Journal of biomedical optics, 2011. 16(3): p. 037004-037004-11.
25. Profio, A.E. and D.R. Doiron, A feasibility study of the use of fluorescence bronchoscopy for localization of small lung tumours. Physics in medicine and biology, 1977. 22(5): p. 949.
26. Chance, B., et al., Oxidation-reduction ratio studies of mitochondria in freeze-trapped samples. NADH and flavoprotein fluorescence signals. Journal of Biological Chemistry, 1979. 254(11): p. 4764-4771.
27. Cai, K., et al., Characterizing Prostate Tumor Mouse Xenografts with CEST and MT-MRI and Redox Scanning, in Oxygen Transport to Tissue XXXIV. 2013, Springer. p. 39-45.
28. Xu, H.N., et al. Calibration of redox scanning for tissue samples. in SPIE BiOS: Biomedical Optics. 2009. International Society for Optics and Photonics.
29. Besl, P.J. and N.D. McKay. Method for registration of 3-D shapes. in Robotics-DL tentative. 1992. International Society for Optics and Photonics.
30. Chen, Y. and G. Medioni, Object modelling by registration of multiple range images. Image and vision computing, 1992. 10(3): p. 145-155.
31. Lodi, G., et al., Interventions for treating oral leukoplakia. Cochrane Database Syst Rev, 2006. 4.