Diagnosing breast cancer using Raman spectroscopy: prospective analysis. Academic Article uri icon

Overview

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.

authors

  • Haka, Abigail Susan
  • Volynskaya, Zoya
  • Gardecki, Joseph A
  • Nazemi, Jon
  • Shenk, Robert
  • Wang, Nancy
  • Dasari, Ramachandra R
  • Fitzmaurice, Maryann
  • Feld, Michael S

publication date

  • January 1, 2009

Research

keywords

  • Algorithms
  • Breast Neoplasms
  • Diagnosis, Computer-Assisted
  • Spectrum Analysis, Raman

Identity

PubMed Central ID

  • PMC2774977

Scopus Document Identifier

  • 74049131740

Digital Object Identifier (DOI)

  • 10.1080/02841860600891325

PubMed ID

  • 19895125

Additional Document Info

volume

  • 14

issue

  • 5