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Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (March 2003).

Publication ,  Journal Article
Palmer, GM; Zhu, C; Breslin, TM; Xu, F; Gilchrist, KW; Ramanujam, N
Published in: IEEE Trans Biomed Eng
November 2003

Nonmalignant (n = 36) and malignant (n = 20) tissue samples were obtained from breast cancer and breast reduction surgeries. These tissues were characterized using multiple excitation wavelength fluorescence spectroscopy and diffuse reflectance spectroscopy in the ultraviolet-visible wavelength range, immediately after excision. Spectra were then analyzed using principal component analysis (PCA) as a data reduction technique. PCA was performed on each fluorescence spectrum, as well as on the diffuse reflectance spectrum individually, to establish a set of principal components for each spectrum. A Wilcoxon rank-sum test was used to determine which principal components show statistically significant differences between malignant and nonmalignant tissues. Finally, a support vector machine (SVM) algorithm was utilized to classify the samples based on the diagnostically useful principal components. Cross-validation of this nonparametric algorithm was carried out to determine its classification accuracy in an unbiased manner. Multiexcitation fluorescence spectroscopy was successful in discriminating malignant and nonmalignant tissues, with a sensitivity and specificity of 70% and 92%, respectively. The sensitivity (30%) and specificity (78%) of diffuse reflectance spectroscopy alone was significantly lower. Combining fluorescence and diffuse reflectance spectra did not improve the classification accuracy of an algorithm based on fluorescence spectra alone. The fluorescence excitation-emission wavelengths identified as being diagnostic from the PCA-SVM algorithm suggest that the important fluorophores for breast cancer diagnosis are most likely tryptophan, NAD(P)H and flavoproteins.

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Published In

IEEE Trans Biomed Eng

DOI

ISSN

0018-9294

Publication Date

November 2003

Volume

50

Issue

11

Start / End Page

1233 / 1242

Location

United States

Related Subject Headings

  • Spectrophotometry, Ultraviolet
  • Spectrometry, Fluorescence
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Principal Component Analysis
  • Predictive Value of Tests
  • Pattern Recognition, Automated
  • Humans
  • Diagnosis, Computer-Assisted
  • Breast Neoplasms
 

Citation

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Palmer, G. M., Zhu, C., Breslin, T. M., Xu, F., Gilchrist, K. W., & Ramanujam, N. (2003). Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (March 2003). IEEE Trans Biomed Eng, 50(11), 1233–1242. https://doi.org/10.1109/TBME.2003.818488
Palmer, Gregory M., Changfang Zhu, Tara M. Breslin, Fushen Xu, Kennedy W. Gilchrist, and Nirmala Ramanujam. “Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (March 2003).IEEE Trans Biomed Eng 50, no. 11 (November 2003): 1233–42. https://doi.org/10.1109/TBME.2003.818488.
Palmer GM, Zhu C, Breslin TM, Xu F, Gilchrist KW, Ramanujam N. Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (March 2003). IEEE Trans Biomed Eng. 2003 Nov;50(11):1233–42.
Palmer, Gregory M., et al. “Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (March 2003).IEEE Trans Biomed Eng, vol. 50, no. 11, Nov. 2003, pp. 1233–42. Pubmed, doi:10.1109/TBME.2003.818488.
Palmer GM, Zhu C, Breslin TM, Xu F, Gilchrist KW, Ramanujam N. Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (March 2003). IEEE Trans Biomed Eng. 2003 Nov;50(11):1233–1242.

Published In

IEEE Trans Biomed Eng

DOI

ISSN

0018-9294

Publication Date

November 2003

Volume

50

Issue

11

Start / End Page

1233 / 1242

Location

United States

Related Subject Headings

  • Spectrophotometry, Ultraviolet
  • Spectrometry, Fluorescence
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Principal Component Analysis
  • Predictive Value of Tests
  • Pattern Recognition, Automated
  • Humans
  • Diagnosis, Computer-Assisted
  • Breast Neoplasms