Discrimination of normal, benign, and malignant breast tissues by Raman spectroscopy

Abstract

Breast cancers are the leading cancers among females. Diagnosis by fine needle aspiration cytology (FNAC) is the gold standard. The widely practiced screening method, mammography, suffers from high false positive results and repeated exposure to harmful ionizing radiation. As with all other cancers survival rates are shown to heavily depend on stage of the cancers (Stage 0, 95%; Stage IV, 75%). Hence development of more reliable screening and diagnosis methodology is of considerable interest in breast cancer management. Raman spectra of normal, benign, and malignant breast tissue show significant differences. Spectral differences between normal and diseased breast tissues are more pronounced than between the two pathological conditions, malignant and benign tissues. Based on spectral profiles, the presence of lipids (1078, 1267, 1301, 1440, 1654, 1746 cm(-1)) is indicated in normal tissue and proteins (stronger amide I, red shifted DeltaCH2, broad and strong amide III, 1002, 1033, 1530, 1556 cm(-1)) are found in benign and malignant tissues. The major differences between benign and malignant tissue spectra are malignant tissues seem to have an excess of lipids (1082, 1301, 1440 cm(-1)) and presence of excess proteins (amide I, amide III, red shifted DeltaCH2, 1033, 1002 cm(-1)) is indicated in benign spectra. The multivariate statistical tool, principal components analysis (PCA) is employed for developing discrimination methods. A score of factor 1 provided a reasonable classification of all three tissue types. The analysis is further fine-tuned by employing Mahalanobis distance and spectral residuals as discriminating parameters. This approach is tested both retrospectively and prospectively. The limit test, which provides the most unambiguous discrimination, is also considered and this approach clearly discriminated all three tissue types. These results further support the efficacy of Raman spectroscopic methods in discriminating normal and diseased breast tissues.

Biochemical correlation of Raman spectra of normal, benign and malignant breast tissues: A spectral deconvolution study

“…These training sets were challenged by large testing data from certified 6,21 as well as 69 (427 spectra) blinded specimen. 6,22 Typical spectra of established Raman models of normal, malignant and benign breast tissues are shown in Figure 1.…”

“…8,23 In our earlier studies, a total of 258 sites/spectra (105 normal, 101 malignant, 52 benign) were recorded on histopathologically certified 29 normal, 24 infiltrating ductal carcinoma and seven fibroadenoma breast tissues. 21 Among them, randomly selected 36 normal, 35 malignant, and 21 benign breast tissue spectra were used for development of spectroscopic models. These models were then verified and evaluated by 69 blinded samples.…”

“…In view of these considerations, we had taken up conventional Raman spectroscopy investigations to classify ex vivo specimen of normal, fibroadenoma and infiltrating ductal carcinoma -the most prevalent benign and malignant conditions, 6 respectively. 21,22 In these studies training sets were developed, verified and evaluated by larger data sets consisting of certified as well as blinded ex vivo specimen. 6,21,22 As a logical and subsequent step, we have attempted to understand and correlate spectral features and possible biochemical changes in normal, benign and malignant breast tissues by curve fitting.…”

“…21,22 In these studies training sets were developed, verified and evaluated by larger data sets consisting of certified as well as blinded ex vivo specimen. 6,21,22 As a logical and subsequent step, we have attempted to understand and correlate spectral features and possible biochemical changes in normal, benign and malignant breast tissues by curve fitting. The findings of curve deconvolution analysis of Raman spectra that are used in the established normal, benign and malignant models are discussed in the present paper.…”

Methods and Applications of Raman Microspectroscopy to Single-Cell Analysis

“…A widely used MVA method to analyze Raman spectra is PCA, because it is readily implemented in most standard software packages for data acquisition and data analysis. [32][33][34] A comprehensive review of PC analysis of Raman spectra can be found elsewhere. 35 PCA applies a set of linear-orthogonal transformations to the data set, which identifies correlated variables in the data and assigns them to new, uncorrelated variables, the socalled PCs.…”