beta-carotene and Carcinoma--Ductal--Breast

This study uses the powerful fingerprint features of Raman spectroscopy to distinguish different types of breast tissues including normal breast tissues (NB), fibroadenoma (FD), atypical ductal hyperplasia (ADH), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). Thin frozen tissue sections of fresh breast tissues were measured by Raman spectroscopy. Due to the inherent low sensitivity of Raman spectra, Au@SiO2 shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) technique was utilized to provide supplementary and more informative spectral features. A total of 619 Raman spectra were acquired and compared to 654 SHINERS spectra. The maximum enhancement effect of distinct and specific bands was characterized for different tissue types. When applying the new criteria, excellent separation of FD, DCIS, and IDC was obtained for all tissue types. Most importantly, we were able to distinguish ADH from DCIS. Although only a preliminary distinction was characterized between ADH and NB, the results provided a good foundation of criteria to further discriminate ADH from NB and shed more light toward a better understanding of the mechanism of ADH formation. This is the first report to detect the premalignant (ADH and DCIS) breast tissue frozen sections and also the first report exploiting SHINERS to detect and distinguish breast tissues. The results presented in this study show that SHINERS can be applied to accurately and efficiently identify breast lesions. Further, the spectra can be acquired in a minimally invasive procedure and analyzed rapidly facilitating early and accurate diagnosis in vivo/in situ.

Topics: Adult; Aged; beta Carotene; Breast; Breast Neoplasms; Carcinoma, Ductal, Breast; Carcinoma, Intraductal, Noninfiltrating; DNA; Female; Fibroadenoma; Gold; Humans; Hyperplasia; Metal Nanoparticles; Middle Aged; Nanotechnology; Silicon Dioxide; Spectrum Analysis, Raman; Young Adult

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.. 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.. 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.

Topics: Adipose Tissue; beta Carotene; Breast; Breast Neoplasms; Carcinoma in Situ; Carcinoma, Ductal, Breast; Carcinoma, Lobular; Female; Fibrocystic Breast Disease; Hemoglobins; Humans; Image Processing, Computer-Assisted; Least-Squares Analysis; Monte Carlo Method; Neoplasms, Fibrous Tissue; Spectrophotometry, Ultraviolet

The effects of vitamins E and E, beta-carotene and selenium on development of N-nitrosobis(2-oxopropyl)amine (BOP)-induced pancreatic tumours in hamsters were investigated. Dietary supplementation of vitamin C, alone as well as in combination with beta-carotene resulted in consistently lower numbers of advanced ductular lesions. The differences with the controls, however, did not reach the level of statistical significance. Beta-Carotene alone demonstrated no inhibitory effect on the development of (pre)neoplastic lesions in the pancreas. Vitamin E or Se, either alone or in combination, had no effect on the development of advanced ductular lesions in BOP-treated hamsters.

Topics: Adenocarcinoma; Animals; Ascorbic Acid; beta Carotene; Body Weight; Carcinogens; Carcinoma, Ductal, Breast; Carotenoids; Cricetinae; Diet; Male; Mesocricetus; Nitrosamines; Organ Size; Pancreatic Neoplasms; Selenium; Vitamin E