Biomedical subjects

Abigail S Haka

Publications and source records attributed to Abigail S Haka.

8 recordsLinked to original sources

PubMed2006

Intrinsic fluorescence and diffuse reflectance spectroscopy identify superficial foam cells in coronary plaques prone to erosion.

OBJECTIVE: Foam cells perform critical functions in atherosclerosis. We hypothesize that coronary segments with superficial foam cells (SFCs) situated in a region of interest with a depth of 200 mum can be identified using intrinsic fluorescence spectroscopy (IFS) and diffuse reflectance spectroscopy (DRS). This is a key step in our ongoing program to develop a spectroscopic technique for real-time in vivo diagnosis of vulnerable atherosclerotic plaque. METHODS AND RESULTS: We subjected 132 human coronary segments to in vitro IFS and DRS. We detected SFCs in 13 thick fibrous cap atheromas and 8 pathologic intimal thickening (PIT) lesions. SFCs colocalized with accumulations of smooth muscle cells and proteoglycans, including hyaluronan (P<0.001). Two spectroscopic parameters were generated from analysis of IFS at 480 nm excitation and DRS. A discriminatory algorithm using these parameters identified specimens with SFC area >40%, 20%, 10%, 5%, 2.5%, and 0% of the region of interest with 98%, 98%, 93%, 94%, 93%, and 90% accuracy, respectively. CONCLUSIONS: Our combined IFS and DRS technique accurately detects SFCs in thick fibrous cap atheromas and PIT lesions. Because SFCs are associated with histological markers of plaque erosion, our spectroscopic technique could prove useful in identifying vulnerable plaques.

Algorithms↗

PubMed2006

In vivo margin assessment during partial mastectomy breast surgery using raman spectroscopy.

We present the first demonstration of in vivo collection of Raman spectra of breast tissue. Raman spectroscopy, which analyzes molecular vibrations, is a promising new technique for the diagnosis of breast cancer. We have collected 31 Raman spectra from nine patients undergoing partial mastectomy procedures to show the feasibility of in vivo Raman spectroscopy for intraoperative margin assessment. The data was fit with an established model, resulting in spectral-based tissue characterization in only 1 second. Application of our previously developed diagnostic algorithm resulted in perfect sensitivity and specificity for distinguishing cancerous from normal and benign tissues in our small data set. Significantly, we have detected a grossly invisible cancer that, upon pathologic review, required the patient to undergo a second surgical procedure. Had Raman spectroscopy been used in a real-time fashion to guide tissue excision during the procedure, the additional reexcision surgery might have been avoided. These preliminary findings suggest that Raman spectroscopy has the potential to lessen the need for reexcision surgeries resulting from positive margins and thereby reduce the recurrence rate of breast cancer following partial mastectomy surgeries.

Breast Neoplasms↗

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

Classification of Fourier transform infrared microscopic imaging data of human breast cells by cluster analysis and artificial neural networks.

Cluster analysis and artificial neural networks (ANNs) are applied to the automated assessment of disease state in Fourier transform infrared microscopic imaging measurements of normal and carcinomatous immortalized human breast cell lines. K-means clustering is used to implement an automated algorithm for the assignment of pixels in the image to cell and non-cell categories. Cell pixels are subsequently classified into carcinoma and normal categories through the use of a feed-forward ANN computed with the Broyden-Fletcher-Goldfarb-Shanno training algorithm. Inputs to the ANN consist of principal component scores computed from Fourier filtered absorbance data. A grid search optimization procedure is used to identify the optimal network architecture and filter frequency response. Data from three images corresponding to normal cells, carcinoma cells, and a mixture of normal and carcinoma cells are used to build and test the classification methodology. A successful classifier is developed through this work, although differences in the spectral backgrounds between the three images are observed to complicate the classification problem. The robustness of the final classifier is improved through the use of a rejection threshold procedure to prevent classification of outlying pixels.

Algorithms↗

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↗

PubMedDate not supplied

Real-time Raman system for in vivo disease diagnosis.

Raman spectroscopy has been well established as a powerful in vitro method for studying biological tissue and diagnosing disease. The recent development of efficient, high-throughput, low-background optical fiber Raman probes provides, for the first time, the opportunity to obtain real-time performance in the clinic. We present an instrument for in vivo tissue analysis which is capable of collecting and processing Raman spectra in less than 2 s. This is the first demonstration that data acquisition, analysis, and diagnostics can be performed in clinically relevant times. The instrument is designed to work with the new Raman probes and includes custom written LabVIEW and Matlab programs to provide accurate spectral calibration, analysis, and diagnosis along with important safety features related to laser exposure. The real-time capabilities of the system were demonstrated in vivo during femoral bypass and breast lumpectomy surgeries. Such a system will greatly facilitate the adoption of Raman spectroscopy into clinical research and practice.

Algorithms↗

PubMedDate not supplied

In vivo Raman spectral pathology of human atherosclerosis and vulnerable plaque.

The rupture of vulnerable atherosclerotic plaque accounts for the majority of clinically significant acute cardiovascular events. Because stability of these culprit lesions is directly related to chemical and morphological composition, Raman spectroscopy may be a useful technique for their study. Recent developments in optical fiber probe technology have allowed for the real-time in vivo Raman spectroscopic characterization of human atherosclerotic plaque demonstrated in this work. We spectroscopically examine 74 sites during carotid endarterectomy and femoral artery bypass surgeries. Of these, 34 are surgically biopsied and examined histologically. Excellent signal-to-noise ratio spectra are obtained in only 1 s and fit with an established model, demonstrating accurate tissue characterization. We also report the first evidence that Raman spectroscopy has the potential to identify vulnerable plaque, achieving a sensitivity and specificity of 79 and 85%, respectively. These initial findings indicate that Raman spectroscopy has the potential to be a clinically relevant diagnostic tool for studying cardiovascular disease.

Algorithms↗