A Comparative Evaluation of Raman and Fluorescence Spectroscopy for Optical Diagnosis of Oral Neoplasia

被引:1
作者
Majumder, S. K. [1 ]
Krishna, H. [1 ]
Sidramesh, M. [1 ]
Chaturvedi, P. [1 ]
Gupta, P. K. [1 ]
机构
[1] Raja Ramanna Ctr Adv Technol, Laser Biomed Applicat & Instrumentat Div, Indore 452013, Madhya Pradesh, India
来源
PHOTONICS 2010: TENTH INTERNATIONAL CONFERENCE ON FIBER OPTICS AND PHOTONICS | 2011年 / 8173卷
关键词
Oral cancer; autofluorescence; Raman spectroscopy; diagnostic algorithm; multi-class classification; sparse multinomial logistic regression (SMLR); maximum representation and discrimination feature (MRDF); DIFFUSE-REFLECTANCE SPECTROSCOPY; AUTOFLUORESCENCE; CANCER; ALGORITHM; MUCOSA;
D O I
10.1117/12.898904
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
We report the results of a comparative evaluation of in vivo fluorescence and Raman spectroscopy for diagnosis of oral neoplasia. The study carried out at Tata Memorial Hospital, Mumbai, involved 26 healthy volunteers and 138 patients being screened for neoplasm of oral cavity. Spectral measurements were taken from multiple sites of abnormal as well as apparently uninvolved contra-lateral regions of the oral cavity in each patient. The different tissue sites investigated belonged to one of the four histopathology categories: 1) squamous cell carcinoma (SCC), 2) oral sub-mucous fibrosis (OSMF), 3) leukoplakia (LP) and 4) normal squamous tissue. A probability based multivariate statistical algorithm utilizing nonlinear Maximum Representation and Discrimination Feature for feature extraction and Sparse Multinomial Logistic Regression for classification was developed for direct multi-class classification in a leave-one-patient-out cross validation mode. The results reveal that the performance of Raman spectroscopy is considerably superior to that of fluorescence in stratifying the oral tissues into respective histopathologic categories. The best classification accuracy was observed to be 90%, 93%, 94%, and 89% for SCC, SMF, leukoplakia, and normal oral tissues, respectively, on the basis of leave-one-patient-out cross-validation, with an overall accuracy of 91%. However, when a binary classification was employed to distinguish spectra from all the SCC, SMF and leukoplakik tissue sites together from normal, fluorescence and Raman spectroscopy were seen to have almost comparable performances with Raman yielding marginally better classification accuracy of 98.5% as compared to 94% of fluorescence.
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页数:7
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