In vivo Raman spectroscopy for detection of oral neoplasia: A pilot clinical study

• Published in: Journal of biophotonics Vol. 7; no. 9; pp. 690 - 702
• Main Authors: Krishna, Hemant, Majumder, Shovan Kumar, Chaturvedi, Pankaj, Sidramesh, Muttagi, Gupta, Pradeep Kumar
• Format: Journal Article
• Language: English
• Published: Berlin WILEY-VCH Verlag 01.09.2014; WILEY‐VCH Verlag; Wiley Subscription Services, Inc
• Subjects: Adult; Algorithms; Female; Histology; Humans; In vivo Raman spectroscopy; Lesions; Male; maximum representation and discrimination feature (MRDF) and sparse multinomial logistic regression (SMLR); Middle Aged; Mouth - cytology; Mouth - pathology; Mouth Neoplasms - diagnosis; Mouth Neoplasms - pathology; multi-class classification; oral lesions; Pilot Projects; probability based non-linear diagnostic algorithm; ROC Curve; Spectroscopy; Spectrum Analysis, Raman; probability based non‐linear diagnostic algorithm; maximum representation and discrimination feature (MRDF) and sparse multinomial logistic regression (SMLR); multi‐class classification; oral lesions; In vivo Raman spectroscopy
• ISSN: 1864-063X; 1864-0648; 1864-0648
• DOI: 10.1002/jbio.201300030

We report a pilot study carried out to evaluate the applicability of in vivo Raman spectroscopy for differential diagnosis of malignant and potentially malignant lesions of human oral cavity in a clinical setting. The study involved 28 healthy volunteers and 171 patients having various lesions of oral cavity. The Raman spectra, measured from multiple sites of normal oral mucosa and of lesions belonging to three histopathological categories, viz. oral squamous cell carcinoma (OSCC), oral submucous fibrosis (OSMF) and leukoplakia (OLK), were subjected to a probability based multivariate statistical algorithm capable of direct multi‐class classification. With respect to histology as the gold standard, the diagnostic algorithm was found to provide an accuracy of 85%, 89%, 85% and 82% in classifying the oral tissue spectra into the four tissue categories based on leave‐one‐subject‐out cross validation. When employed for binary classification, the algorithm resulted in a sensitivity and specificity of 94% in discriminating normal from the rest of the abnormal spectra of OSCC, OSMF and OLK tissue sites pooled together. (© 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)