Anatomy-Based Algorithms for Detecting Oral Cancer Using Reflectance and Fluorescence Spectroscopy

• Main Authors: McGee, Sasha (Contributor), Mardirossian, Vartan (Author), Elackattu, Alphi (Author), Mirkovic, Jelena (Contributor), Pistey, Robert (Author), Gallagher, George (Author), Kabani, Sadru (Author), Yu, Chung-Chieh (Contributor), Wang, Zimmern (Author), Badizadegan, Kamran (Contributor), Grillone, Gregory (Author), Feld, Michael S. (Contributor)
• Other Authors: Harvard University- (Contributor), Massachusetts Institute of Technology. Spectroscopy Laboratory (Contributor)
• Format: Article
• Language: English
• Published: Annals Publishing Company, 2011-12-20T18:31:02Z.
• Subjects: Article
• Online Access: Get fulltext

OBJECTIVES: We used reflectance and fluorescence spectroscopy to noninvasively and quantitatively distinguish benign from dysplastic/malignant oral lesions. We designed diagnostic algorithms to account for differences in the spectral properties among anatomic sites (gingiva, buccal mucosa, etc). METHODS: In vivo reflectance and fluorescence spectra were collected from 71 patients with oral lesions. The tissue was then biopsied and the specimen evaluated by histopathology. Quantitative parameters related to tissue morphology and biochemistry were extracted from the spectra. Diagnostic algorithms specific for combinations of sites with similar spectral properties were developed. RESULTS: Discrimination of benign from dysplastic/malignant lesions was most successful when algorithms were designed for individual sites (area under the receiver operator characteristic curve [ROC-AUC],0.75 for the lateral surface of the tongue) and was least accurate when all sites were combined (ROC-AUC, 0.60). The combination of sites with similar spectral properties (floor of mouth and lateral surface of the tongue) yielded an ROC-AUC of 0.71. CONCLUSIONS: Accurate spectroscopic detection of oral disease must account for spectral variations among anatomic sites. Anatomy-based algorithms for single sites or combinations of sites demonstrated good diagnostic performance in distinguishing benign lesions from dysplastic/malignant lesions and consistently performed better than algorithms developed for all sites combined.
National Institutes of Health (U.S) (R0I-CA097966)
National Institutes of Health (U.S) (P4I-RR02594- 21)