Graduation Year


Document Type




Degree Granting Department

Pathology and Laboratory Medicine

Major Professor

Santo V. Nicosia, M.D.

Co-Major Professor

Ira W. Levin, Ph.D.

Committee Member

Wenlong Bai, Ph.D.

Committee Member

Luis H. Garcia-Rubio, Ph.D.

Committee Member

Maria Kallergi, Ph.D.

Committee Member

Patricia A. Kruk, Ph.D.


FT-IR, adenocarcinoma, vibrational, spectroscopy, classification


Vibrational spectroscopic imaging techniques have emerged as powerful methods of obtaining sensitive spatially resolved molecular information from microscopic samples. The data obtained from such techniques reflect the intrinsic molecular chemistry of the sample and in particular yield a wealth of information regarding functional groups which comprise the majority of important molecules found in cells and tissue. These spectroscopic imaging techniques also have the advantage of acquisition of large numbers of spectral measurements which allow statistical analysis of spectral features which are characteristic of the normal histological state as well as different pathologic disease states. Databases of large numbers of samples can be acquired and used to build model systems that can be used to predict spatial properties of unknown samples.

The successful construction and application of such a model system relies on the ability to compile high-quality spectral database information on a large number of samples with minimal sample-to-sample preparation artifact. Tissue microarrays provide a consistent sample preparation for high-throughput infrared spectroscopic profiling of histologic specimens. Tissue arrays consisting of representative normal healthy prostate tissue as well as pathologic entities including prostatitis, benign prostatic hypertrophy, and prostatic adenocarcinoma were constructed and used as sample populations for infrared spectroscopic imaging at high spatial and spectral resolutions.

Histological and pathological features of the imaged tissue were correlated with consecutive tissue sections stained with standard histologic stains and visualized via traditional optical microscopy and reviewed with a trained pathologist. Spectral analysis of histologic class mean spectra and subsequent cross-sample statistical validation were used to classify reliable spectral metrics for class discrimination. Multivariate Gaussian maximum likelihood classification algorithms were used to reliably classify all pixels in an image scene to one of six different histologic subclasses: epithelium, smooth muscular stroma, fibrous stroma, corpora amylacea, lymphocytic infiltration, and blood. The developed database-dependent classification methods were used as a tool to investigate subsequent microarrays designed with both normal epithelial tissue as well as adenocarcinoma from a large population of patients. Such investigation led to the identification of spectral features that proved useful in the preliminary discrimination of benign and malignant prostatic epithelial tissue.