Non-invasive detection of oral cancer using reflectance and fluorescence spectroscopy

In vivo reflectance and fluorescence spectra were collected from patients with oral lesions, as well as healthy volunteers, in order to evaluate the potential of spectroscopy to serve as a non-invasive tool for the detection oral cancer. A total of 710 spectra were analyzed from 79 healthy volunteers, and 87 spectra from 67 patients. Physical models were applied to the measured spectral data in order to extract quantitative parameters relating to the structural and biochemical properties of the tissue. Data collected from healthy volunteers were used to characterize the relationship between the spectral parameters and tissue anatomy. Diagnostic algorithms for distinguishing various lesion categories were then developed using data collected from patients. The healthy volunteer study demonstrated that tissue anatomy has a strong influence on the spectral parameters. Anatomic sites could be easily distinguished from each other despite the apparent overlap in their parameter distributions. In particular, keratinized sites (gingiva and hard palate) were significantly distinct from other anatomic sites. The results of this study provide strong evidence that a robust and accurate spectroscopic based diagnostic algorithm for oral cancer needs to be applied in a site specific manner. Spectral diagnostic algorithms were developed using the data collected from patients, in combination with the data collected from healthy volunteers. The diagnostic performance of the algorithms was evaluated using the area under a receiver operator characteristic curve (ROC-AUC) and the sensitivity and specificity. The diagnostic algorithms were most successful when developed and applied to data collected from a single anatomic site or spectrally similar sites, and when distinguishing visibly normal mucosa from lesions.

Department dc:contributor.department

Harvard University--MIT Division of Health Sciences and Technology.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• McGee, Sasha Alanda

Advisor dc:contributor.advisor

• Michael Feld.

• Harvard University--MIT Division of Health Sciences and Technology.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/43869

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/43869