Diagnosing breast cancer using Raman spectroscopy: prospective analysis

Abstract

Abigail S. Haka, Zoya Volynskaya, Joseph A. Gardecki, and Jon Nazemi Massachusetts Institute of Technology, George R. Harrison Spectroscopy Laboratory, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139

Robert Shenk and Nancy Wang University Hospitals Case Medical Center and Case Western Reserve, 11100 Euclid Avenue, Cleveland Ohio 44106

Ramachandra R. Dasari Massachusetts Institute of Technology, George R. Harrison Spectroscopy Laboratory, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139

Maryann Fitzmaurice University Hospitals Case Medical Center and Case Western Reserve, 11100 Euclid Avenue, Cleveland Ohio 44106

Michael S. Feld Massachusetts Institute of Technology, George R. Harrison Spectroscopy Laboratory, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 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.