Diagnosis of breast cancer using diffuse reflectance spectroscopy: Comparison of a Monte Carlo versus partial least squares analysis based feature extraction technique

Lasers Surg Med. 2006 Aug;38(7):714-24. doi: 10.1002/lsm.20356.


Background and objective: We explored the use of diffuse reflectance spectroscopy in the ultraviolet-visible (UV-VIS) spectrum for the diagnosis of breast cancer. A physical model (Monte Carlo inverse model) and an empirical model (partial least squares analysis) based approach, were compared for extracting diagnostic features from the diffuse reflectance spectra.

Study design/methods: The physical model and the empirical model were employed to extract features from diffuse reflectance spectra measured from freshly excised breast tissues. A subset of extracted features obtained using each method showed statistically significant differences between malignant and non-malignant breast tissues. These features were separately input to a support vector machine (SVM) algorithm to classify each tissue sample as malignant or non-malignant.

Results and conclusions: The features extracted from the Monte Carlo based analysis were hemoglobin saturation, total hemoglobin concentration, beta-carotene concentration and the mean (wavelength averaged) reduced scattering coefficient. Beta-carotene concentration was positively correlated and the mean reduced scattering coefficient was negatively correlated with percent adipose tissue content in normal breast tissues. In addition, there was a statistically significant decrease in the beta-carotene concentration and hemoglobin saturation, and a statistically significant increase in the mean reduced scattering coefficient in malignant tissues compared to non-malignant tissues. The features extracted from the partial least squares analysis were a set of principal components. A subset of principal components showed that the diffuse reflectance spectra of malignant breast tissues displayed an increased intensity over wavelength range of 440-510 nm and a decreased intensity over wavelength range of 510-600 nm, relative to that of non-malignant breast tissues. The diagnostic performance of the classification algorithms based on both feature extraction techniques yielded similar sensitivities and specificities of approximately 80% for discriminating between malignant and non-malignant breast tissues. While both methods yielded similar classification accuracies, the model based approach provided insight into the physiological and structural features that discriminate between malignant and non-malignant breast tissues.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adipose Tissue / pathology
  • Breast / pathology
  • Breast Neoplasms / diagnosis*
  • Breast Neoplasms / pathology
  • Carcinoma in Situ / diagnosis
  • Carcinoma in Situ / pathology
  • Carcinoma, Ductal, Breast / diagnosis
  • Carcinoma, Ductal, Breast / pathology
  • Carcinoma, Lobular / diagnosis
  • Carcinoma, Lobular / pathology
  • Female
  • Fibrocystic Breast Disease / diagnosis
  • Fibrocystic Breast Disease / pathology
  • Hemoglobins / analysis
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Least-Squares Analysis
  • Monte Carlo Method
  • Neoplasms, Fibrous Tissue / diagnosis
  • Neoplasms, Fibrous Tissue / pathology
  • Spectrophotometry, Ultraviolet / statistics & numerical data*
  • beta Carotene / analysis


  • Hemoglobins
  • beta Carotene