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Journal of Biomedical Optics

Diagnosing breast cancer using diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy
Author(s): Zoya I. Volynskaya; Abigail S. Haka; Kate L. Bechtel; Maryann Fitzmaurice; Robert Shenk; Nancy Wang; Jonathan Nazemi; Ramachandra R. Dasari; Michael S. Feld
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Paper Abstract

Using diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy, we have developed an algorithm that successfully classifies normal breast tissue, fibrocystic change, fibroadenoma, and infiltrating ductal carcinoma in terms of physically meaningful parameters. We acquire 202 spectra from 104 sites in freshly excised breast biopsies from 17 patients within 30 min of surgical excision. The broadband diffuse reflectance and fluorescence spectra are collected via a portable clinical spectrometer and specially designed optical fiber probe. The diffuse reflectance spectra are fit using modified diffusion theory to extract absorption and scattering tissue parameters. Intrinsic fluorescence spectra are extracted from the combined fluorescence and diffuse reflectance spectra and analyzed using multivariate curve resolution. Spectroscopy results are compared to pathology diagnoses, and diagnostic algorithms are developed based on parameters obtained via logistic regression with cross-validation. The sensitivity, specificity, positive predictive value, negative predictive value, and overall diagnostic accuracy (total efficiency) of the algorithm are 100, 96, 69, 100, and 91%, respectively. All invasive breast cancer specimens are correctly diagnosed. The combination of diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy yields promising results for discrimination of breast cancer from benign breast lesions and warrants a prospective clinical study.

Paper Details

Date Published: 1 March 2008
PDF: 9 pages
J. Biomed. Opt. 13(2) 024012 doi: 10.1117/1.2909672
Published in: Journal of Biomedical Optics Volume 13, Issue 2
Show Author Affiliations
Zoya I. Volynskaya, Massachusetts Institute of Technology (United States)
Abigail S. Haka, Massachusetts Institute of Technology (United States)
Kate L. Bechtel, Massachusetts Institute of Technology (United States)
Maryann Fitzmaurice, Case Western Reserve Univ. (United States)
Robert Shenk, Univ. Hospitals of Cleveland (United States)
Nancy Wang, Univ. Hospitals of Cleveland (United States)
Jonathan Nazemi, Prescient Medical, Inc. (United States)
Ramachandra R. Dasari, Massachusetts Institute of Technology (United States)
Michael S. Feld, Massachusetts Institute of Technology (United States)


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