Jayanthi J L, Mallia Rupananda J, Shiny Sara Thomas, Baiju Kamalsanan V, Mathews Anitha, Kumar Rejnish, Sebastian Paul, Madhavan Jayaprakash, Aparna G N, Subhash Narayanan
Biophotonics Laboratory, Centre for Earth Science Studies, Kerala, India.
Lasers Surg Med. 2009 Jul;41(5):345-52. doi: 10.1002/lsm.20771.
Low survival rate of individuals with oral cancer emphasize the significance of early detection and treatment. Optical spectroscopic techniques are under various stages of development for diagnosis of epithelial neoplasm. This study evaluates the potential of a multivariate statistical algorithm to classify oral mucosa from autofluorescence spectral features recorded in vivo.
STUDY DESIGN/METHODS: Autofluorescence spectra were recorded in a clinical trial from 15 healthy volunteers and 34 patients with diode laser excitation (404 nm) and pre-processed by normalization, mean-scaling and its combination. Linear discriminant analysis (LDA) based on leave-one-out (LOO) method of cross validation was performed on spectral data for tissue characterization. The sensitivity and specificity were determined for different lesion pairs from the scatter plot of discriminant function scores.
Autofluorescence spectra of healthy volunteers consists of a broad emission at 500 nm that is characteristic of endogenous fluorophores, whereas in malignant lesions three additional peaks are observed at 635, 685, and 705 nm due to the accumulation of porphyrins in oral lesions. It was observed that classification design based on discriminant function scores obtained by LDA-LOO method was able to differentiate pre-malignant dysplasia from squamous cell carcinoma (SCC), benign hyperplasia from dysplasia and hyperplasia from normal with overall sensitivities of 86%, 78%, and 92%, and specificities of 90%, 100%, and 100%, respectively.
The application of LDA-LOO method on the autofluorescence spectra recorded during a clinical trial in patients was found suitable to discriminate oral mucosal alterations during tissue transformation towards malignancy with improved diagnostic accuracies.
口腔癌患者生存率较低，凸显了早期检测与治疗的重要性。光学光谱技术正处于上皮肿瘤诊断的不同发展阶段。本研究评估了一种多元统计算法根据体内记录的自体荧光光谱特征对口腔黏膜进行分类的潜力。
研究设计/方法：在一项临床试验中，用二极管激光（404nm）激发，记录了15名健康志愿者和34名患者的自体荧光光谱，并通过归一化、均值缩放及其组合进行预处理。对光谱数据进行基于留一法（LOO）交叉验证的线性判别分析（LDA），以进行组织特征分析。根据判别函数得分的散点图，确定不同病变对的敏感性和特异性。
健康志愿者的自体荧光光谱在500nm处有一个宽发射峰，这是内源性荧光团的特征，而在恶性病变中，由于口腔病变中卟啉的积累，在635、685和705nm处观察到另外三个峰。观察发现，基于LDA-LOO方法获得的判别函数得分的分类设计能够区分癌前发育异常与鳞状细胞癌（SCC）、良性增生与发育异常以及增生与正常组织，总体敏感性分别为86%、78%和92%，特异性分别为90%、100%和100%。
在患者临床试验期间记录的自体荧光光谱上应用LDA-LOO方法，被发现适合于在组织向恶性转化过程中鉴别口腔黏膜改变，诊断准确性有所提高。
