Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (March 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.
Palmer
Gregory M
GM
Department of Biomedical Engineering, University of Wisconsin, Madison, WI 53706, USA. gmpalmer@wisc.edu
Zhu
Changfang
C
Breslin
Tara M
TM
Xu
Fushen
F
Gilchrist
Kennedy W
KW
Ramanujam
Nirmala
N
D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification* / Q000175:diagnosis* / Q000473:pathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013050:Spectrometry, Fluorescence / Q000379:methods*; D013056:Spectrophotometry, Ultraviolet / Q000379:methods*