Biomedical subjects

Karen E Shafer-Peltier

Publications and source records attributed to Karen E Shafer-Peltier.

4 recordsLinked to original sources

PubMed2005

Diagnosing breast cancer by using Raman spectroscopy.

We employ Raman spectroscopy to diagnose benign and malignant lesions in human breast tissue based on chemical composition. In this study, 130 Raman spectra are acquired from ex vivo samples of human breast tissue (normal, fibrocystic change, fibroadenoma, and infiltrating carcinoma) from 58 patients. Data are fit by using a linear combination model in which nine basis spectra represent the morphologic and chemical features of breast tissue. The resulting fit coefficients provide insight into the chemical/morphological makeup of the tissue and are used to develop diagnostic algorithms. The fit coefficients for fat and collagen are the key parameters in the resulting diagnostic algorithm, which classifies samples according to their specific pathological diagnoses, attaining 94% sensitivity and 96% specificity for distinguishing cancerous tissues from normal and benign tissues. The excellent results demonstrate that Raman spectroscopy has the potential to be applied in vivo to accurately classify breast lesions, thereby reducing the number of excisional breast biopsies that are performed.

Adipose Tissue↗

PubMed2003

Toward a glucose biosensor based on surface-enhanced Raman scattering.

This work presents the first step toward a glucose biosensor using surface-enhanced Raman spectroscopy (SERS). Historically, glucose has been extremely difficult to detect by SERS because it has a small normal Raman cross section and adsorbs weakly or not at all to bare silver surfaces. In this paper, we report the first systematic study of the direct detection of glucose using SERS. Glucose is partitioned into an alkanethiol monolayer adsorbed on a silver film over nanosphere (AgFON) surface and thereby, it is preconcentrated within the 0-4 nm thick zone of electromagnetic field enhancement. The experiments presented herein utilize leave-one-out partial least-squares (LOO-PLS) analysis to demonstrate quantitative glucose detection both over a large (0-250 mM) and clinically relevant (0-25 mM) concentration range. The root-mean-squared error of prediction (RMSEP) of 1.8 mM (33.1 mg/dL) in the clinical study is near that desired for medical applications (1 mM, 18 mg/dL). Future studies will advance toward true in vivo, real time, minimally invasive sensing.

Biosensing Techniques↗

PubMed2002

Identifying microcalcifications in benign and malignant breast lesions by probing differences in their chemical composition using Raman spectroscopy.

We have applied Raman spectroscopy to analyze the chemical composition of microcalcifications occurring in benign and malignant lesions in the human breast. Microcalcifications were initially separated into two categories based on their Raman spectrum: type I, calcium oxalate dihydrate, and type II, calcium hydroxyapatite. Type I microcalcifications were diagnosed as benign, whereas type II were subdivided into benign and malignant categories using principal component analysis, a statistical technique. Although type II microcalcifications are primarily composed of calcium hydroxyapatite, they also contain trace amounts of several biological impurities. Using principal component analysis, we were able to highlight subtle chemical differences in type II microcalcifications that correlate with breast disease. On the basis of these results, we believe that type II microcalcifications formed in benign ducts typically contain a larger amount of calcium carbonate and a smaller amount of protein than those formed in malignant ducts. Using this diagnostic strategy, we were able to distinguish microcalcifications occurring in benign and malignant ducts with a sensitivity of 88% and a specificity of 93%. This is a significant improvement over current X-ray mammography techniques, which are unable to reliably differentiate microcalcifications in benign and malignant breast lesions.

Adult↗

PubMed2002

Model-based biological Raman spectral imaging.

Raman spectral imaging is a powerful tool for determining chemical information in a biological specimen. The challenge is to condense the large amount of spectral information into an easily visualized form with high information content. Researchers have applied a range of techniques, from peak-height ratios to sophisticated models, to produce interpretable Raman images. The purpose of this article is to review some of the more common imaging approaches, in particular principal components analysis, multivariate curve resolution, and Euclidean distance, as well as to present a new technique, morphological modeling. How to best extract meaningful chemical information using each imaging approach will be discussed and examples of images produced with each will be shown.

Algorithms↗