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In vivo subsite classification and diagnosis of oral cancers using Raman spectroscopy

Journal of Innovative Optical Health Sciences 2016年第5期9卷 57-70页

作者： Aditi Sahu Atul Deshmukh Arti R.Hole Pankaj Chaturvedi cmurali krishna Chilakapati Laboratory ACTRECTMC KhargharNavi Mumbai 410210India Head and Neck Surgical Oncology Tata Memorial HospitalDr.E.Borges RoadParelMumbaiMaharashtra-400012India

Oral cancers suffer from poor disease free survival rates due to delayed ***,rapid,objective approaches as adjuncts to visual inspection can help in better management of oral *** spectrosoopy(RS)has shown potential in identification of oral premalignant and malignant conditions and also in the detection of early cancer changes like cancer field ffects(CFE)at buccal mucosa *** differences between different oral subsites have also been reported using *** this study,anatomical differences between subsites and their possible infuence on healthy vs pathological classification were evaluated on 85 oral cancer and 72 healthy *** were acquired from buccal mucosa,lip and tongue in healthy,contralateral(internal healthy control),premalignant and cancer conditions using fiber optic Raman *** spectra indicate predominance of lipids in healthy buccal mucosa,contribution of both lipids and proteins in lip while major dominance of protein in tongue *** healthy to tumor,changes in protein secondary structure,DNA and heme related features were *** component linear discriminant analysis(PC-LDA)followed by leave one out CrOoss-validation(LOOCV)was used for data *** gs indicate buccal mucosa and tongue are distinct entities,while lip misclassifies with both these ***,the diagnostic algorithm for individual subsites gave improved classification efficiencies with respect to the pooled subsites ***,as the pooled subsites model yielded 98%specifcity and 100%sensitivity,this model may be more useful for preliminary screening ***-scale validation studies are a pre requisite before envisaging future clinical applications.

关键词： Oral cancer screening in vivo Raman spectroscopy PC-LDA.

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