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

• Published in: IEEE transactions on biomedical engineering 2003-11, Vol.50 (11), p.1233-1242
• Main Authors: Palmer, G.M., Changfang Zhu, Breslin, T.M., Fushen Xu, Gilchrist, K.W., Ramanujam, N.
• Format: Article
• Language: English
• Subjects: Algorithms; Biological and medical sciences; Breast cancer; Breast Neoplasms - classification; Breast Neoplasms - diagnosis; Breast Neoplasms - pathology; Diagnosis, Computer-Assisted - methods; Fluorescence; Humans; Medical sciences; Pattern Recognition, Automated; Predictive Value of Tests; Principal Component Analysis; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Reflectivity; Reproducibility of Results; Sensitivity and Specificity; Spectrometry, Fluorescence - methods; Spectrophotometry, Ultraviolet - methods; Spectroscopy; Spectrum analysis; Studies; Support vector machine classification; Support vector machines; Surges; Technology. Biomaterials. Equipments. Material. Instrumentation; Testing
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2003.818488

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.