Affiliations: Chilakapati Laboratory, ACTREC, Tata Memorial Center, Navi Mumbai, India | Vaidya Laboratory, ACTREC, Tata Memorial Center, Navi Mumbai, India
Note:  Corresponding author: Dr. C. Murali Krishna, Scientific Officer ‘F’ and Principal Investigator, Chilakapati Laboratory, Advanced Center for Treatment Research and Education in Cancer (ACTREC), Tata Memorial Center (TMC), Kharghar, Sector ‘22’, Navi Mumbai 410210, India. Tel.: +91 22 2740 5039; E-mails: [email protected], [email protected]
Abstract: BACKGROUND: Early detection of oral cancers can lead to improved survival rates. Due to limitations of existing methods, alternative approaches like Raman spectroscopy are therefore being explored. Ex vivo approaches are more suitable as they obviate need of on-site instrumentation and stringent experimental conditions. Serum Raman spectroscopy has shown potential in detecting cancers like cervical, breast, colorectal and head and neck cancers. Feasibility of classification of normal and oral cancer using serum Raman spectroscopy with 532 nm excitation has also been explored. OBJECTIVE: In the present study, feasibility of differentiating normal and cancer serum samples using 785 nm excitation laser – the most widely used laser for biomedical applications was explored. METHODS: 36 buccal mucosa, 33 tongue cancer patients and 17 healthy subjects were recruited and Raman spectra of sera were recorded using assembled Raman microprobe coupled with 40× objective. To eliminate heterogeneity, average of 3 spectra recorded from each sample was subjected to PCA and PC-LDA followed by leave-one-out cross-validation. RESULTS: Findings indicate average classification efficiency of ~78% for normal and cancer. Buccal mucosa and tongue cancer serum could also be classified with an efficiency of ~68%. CONCLUSIONS: Findings of the study corroborate with the previous study and indicate potential of this approach in management of oral cancer in future, after prospective validation.
Keywords: Oral cancer, Raman spectroscopy, serum, principal component analysis (PCA), principal component-linear discriminant analysis (PC-LDA)