找出口腔癌組織交大陽明「拉曼光譜」提高手術精準度記者蕭玗欣／台北報導 台灣罹患口腔癌人數近10年增加2倍，每年約有5400名新確診個案，不僅逐年增加更有年輕化趨勢，高居男性腫瘤死亡原因第四位。交通大學應用化學系暨分子與科學研究所濵口宏夫講座教授、陽明大學生醫光電研究所邱爾德教授與台中榮民總醫院共同合作，以分子光譜研發自動檢測口腔癌組織分析方法，將可協助臨床醫師診斷病情，進一步提升手術精準度。該技術開發由濵口宏夫教授、邱爾德教授共同指導陽明大學生醫光電所博士生陳柏熊，並與研究室團隊成員島田林太郎博士、藪本宗士博士、安藤正浩博士及台中榮總牙科部黃穰基主任、李立慈醫師進行跨校、跨國、跨領域合作。研究成果在今年一月發表於《Nature》旗下的科學報導《Scientific Reports》國際期刊。交大表示，分子的拉曼光譜可視為每個分子獨有的分子指紋，如同每個人皆有自己專屬、獨一無二的指紋；而生物組織中的分子組成更為複雜，因此，如何將生物組織的拉曼光譜進行有效的分析，讓臨床醫師、病理師在沒有光譜學知識的基礎下即可進行癌症輔助診斷，一直是光譜學家努力的目標。有鑑於此，結合交大、陽明與台中榮總，學、醫界三方的合作團隊利用拉曼光譜技術結合多變數分析法，分解口腔癌組織中大量增加的角蛋白分子的拉曼光譜，並進一步定量、比較角蛋白分子組成成分在正常組織與病變組織的純度，結果顯示癌化口腔組織中的角蛋白分子成分純度較高。未來病理師使用攜帶型拉曼儀器檢測組織再以軟體進行分析，按下檢測按鈕即可自動分辨出正常組織與病變組織，操作方法簡易又準確。團隊表示，此一方法可正確辨識正常與口腔癌組織，人體試驗與臨床應用是下階段目標，期許未來實踐於口腔癌手術過程中同步判斷病變區域並進行切除，提高手術精準度與醫療效率。
Automatic and objective oral cancer diagnosis by Raman spectroscopic detection of keratin with multivariate curve resolution analysis Scientific Reports 6, Article number: 20097 (2016)….Spectroscopic methods for cancer diagnosis have made a rapid progress in recent years. In particular, Raman spectroscopy has been proven to be effective for discriminating cancerous against normal oral tissues9,10,11,12,13,14,15,16,17,18,19. Spectroscopic discriminations of cancer tissues in these previous studies are mostly based on Principal Component Analysis (PCA) in conjunction with statistical multi-parameter analyses. The key advantage of PCA is that once a spectral data set obtained from tissues is analyzed and separated into several particular categories, then a new spectrum can automatically be assigned to one of those categories, for example, cancerous vs. normal. However, PCA does not extract detailed molecular spectral information from the categorized spectra and its physical basis of categorization tends to remain unclear. Biological tissues are highly heterogeneous and their Raman spectra vary widely depending on the position where they are measured. Furthermore, molecular compositions of tissues are so complicated that their raw Raman spectra can hardly be interpreted. In order to accomplish global tissue analysis effective for cancer diagnosis, we need to (1) collect Raman spectra from as many as possible points from a tissue sample, (2) estimate the number of principal spectral components contained in this large number of Raman spectra, (3) decompose the raw spectra into spectrally interpretable components and finally (4) objectively characterize tissues according to the extracted spectral information. The methodology employed up to now relies greatly on specialized "spectroscopic eyes", which has not facilitated its practical applications in cancer diagnosis. The aim of the present study is to develop an automatic and objective method for discriminating oral cancer tissue by detecting keratin without any specialized knowledge of spectroscopy. We (1) collected a total of 196 Raman spectra from one oral tissue sample, (2) estimated the number of principal spectral components by Singular Value Decomposition (SVD), (3) applied Multivariate Curve Resolution-Alternating Least Square (MCR-ALS)20,21 analysis to decompose a large set of complicated spectra into spectrally interpretable components and (4) carried out the spectral matching analysis between these MCR-decomposed spectral components and the keratin standard spectrum, to objectively discriminate OSCC against normal tissues via Unit Normalized Euclidean Distance (UNED). The present method fully utilizes the Raman spectral information (molecular fingerprint) of the marker molecule, keratin; in contrast, in PCA approaches, Raman spectra are treated just as two-dimensional signature for a pattern recognition analysis without referring much to their physicochemical meanings. The identification of keratin signature is automatically and objectively achieved with the use of UNED, making the whole analysis readily acceptable for non-specialists of spectroscopy.
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