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

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

The color of cancer: margin guidance for oral cancer resection using elastic scattering spectroscopy

Published in final edited form as: Laryngoscope. 2017 September ; 127(Suppl 4): S1–S9. doi:10.1002/lary.26763.OBJECTIVES/HYPOTHESIS: To evaluate the usefulness of elastic scattering spectroscopy (ESS) as a diagnostic adjunct to frozen section analysis in patients with diagnosed squamous cell carcinoma of the oral cavity. STUDY DESIGN: Prospective analytic study. METHODS: Subjects for this single institution, institutional review board-approved study were recruited from among patients undergoing surgical resection for squamous cell cancer of the oral cavity. A portable ESS device with a contact fiberoptic probe was used to obtain spectral signals. Four to 10 spectral readings were obtained on each subject from various sites including gross tumor and normal-appearing mucosa in the surgical margin. Each reading was correlated with the histopathologic findings of biopsies taken from the exact location of the spectral readings. A diagnostic algorithm based on multidimensional pattern recognition/machine learning was developed. Sensitivity and specificity, error rate, and area under the curve were used as performance metrics for tests involving classification between disease and nondisease classes. RESULTS: Thirty-four (34) subjects were enrolled in the study. One hundred seventy-six spectral data point/biopsy specimen pairs were available for analysis. ESS distinguished normal from abnormal tissue, with a sensitivity ranging from 84% to 100% and specificity ranging from 71% to 89%, depending on how the cutoff between normal and abnormal tissue was defined (i.e., mild, moderate, or severe dysplasia). There were statistically significant differences in malignancy scores between histologically normal tissue and invasive cancer and between noninflamed tissue and inflamed tissue. CONCLUSIONS: This is the first study to evaluate the effectiveness of ESS in guiding mucosal resection margins in oral cavity cancer. ESS provides fast, real-time assessment of tissue without the need for pathology expertise. ESS appears to be effective in distinguishing between normal mucosa and invasive cancer and between "normal" tissue (histologically normal and mild dysplasia) and "abnormal" tissue (severe dysplasia and carcinoma in situ) that might require further margin resection. Further studies, however, are needed with a larger sample size to validate these findings and to determine the effectiveness of ESS in distinguishing visibly and histologically normal tissue from visibly normal but histologically abnormal tissue. LEVEL OF EVIDENCE: NA Laryngoscope, 127:S1-S9, 2017.R21 DE023192 - NIDCR NIH HHSAccepted manuscrip

Model-based spectroscopic analysis of the oral cavity: impact of anatomy

In order to evaluate the impact of anatomy on the spectral properties of oral tissue, we used reflectance and fluorescence spectroscopy to characterize nine different anatomic sites. All spectra were collected in vivo from healthy oral mucosa. We analyzed 710 spectra collected from the oral cavity of 79 healthy volunteers. From the spectra, we extracted spectral parameters related to the morphological and biochemical properties of the tissue. The parameter distributions for the nine sites were compared, and we also related the parameters to the physical properties of the tissue site. k-Means cluster analysis was performed to identify sites or groups of sites that showed similar or distinct spectral properties. For the majority of the spectral parameters, certain sites or groups of sites exhibited distinct parameter distributions. Sites that are normally keratinized, most notably the hard palate and gingiva, were distinct from nonkeratinized sites for a number of parameters and frequently clustered together. The considerable degree of spectral contrast (differences in the spectral properties) between anatomic sites was also demonstrated by successfully discriminating between several pairs of sites using only two spectral parameters. We tested whether the 95% confidence interval for the distribution for each parameter, extracted from a subset of the tissue data could correctly characterize a second set of validation data. Excellent classification accuracy was demonstrated. Our results reveal that intrinsic differences in the anatomy of the oral cavity produce significant spectral contrasts between various sites, as reflected in the extracted spectral parameters. This work provides an important foundation for guiding the development of spectroscopic-based diagnostic algorithms for oral cancer.National Institutes of Health (Grants No. CA097966-03 and No. P41 RR02594-21