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Journal of Biomedical Optics

Diagnosing breast cancer using Raman spectroscopy: prospective analysis
Author(s): Abigail S. Haka; Zoya I. Volynskaya; Joseph A. Gardecki; Jonathan Nazemi; Robert Shenk; Nancy Wang; Ramachandra Rao Dasari; Maryann Fitzmaurice; Michael S. Feld
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Paper Abstract

We present the first prospective test of Raman spectroscopy in diagnosing normal, benign, and malignant human breast tissues. Prospective testing of spectral diagnostic algorithms allows clinicians to accurately assess the diagnostic information contained in, and any bias of, the spectroscopic measurement. In previous work, we developed an accurate, internally validated algorithm for breast cancer diagnosis based on analysis of Raman spectra acquired from fresh-frozen in vitro tissue samples. We currently evaluate the performance of this algorithm prospectively on a large ex vivo clinical data set that closely mimics the in vivo environment. Spectroscopic data were collected from freshly excised surgical specimens, and 129 tissue sites from 21 patients were examined. Prospective application of the algorithm to the clinical data set resulted in a sensitivity of 83%, a specificity of 93%, a positive predictive value of 36%, and a negative predictive value of 99% for distinguishing cancerous from normal and benign tissues. The performance of the algorithm in different patient populations is discussed. Sources of bias in the in vitro calibration and ex vivo prospective data sets, including disease prevalence and disease spectrum, are examined and analytical methods for comparison provided.

Paper Details

Date Published: 1 September 2009
PDF: 8 pages
J. Biomed. Opt. 14(5) 054023 doi: 10.1117/1.3247154
Published in: Journal of Biomedical Optics Volume 14, Issue 5
Show Author Affiliations
Abigail S. Haka, Weill Cornell Medical College (United States)
Zoya I. Volynskaya, Massachusetts Institute of Technology (United States)
Joseph A. Gardecki, Wellman Ctr. for Photomedicine (United States)
Jonathan Nazemi, Prescient Medical, Inc. (United States)
Robert Shenk, Univ. Hospitals of Cleveland (United States)
Nancy Wang, Univ. Hospitals of Cleveland (United States)
Ramachandra Rao Dasari, Massachusetts Institute of Technology (United States)
Maryann Fitzmaurice, Case Western Reserve Univ. (United States)
Michael S. Feld, Massachusetts Institute of Technology (United States)

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