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Prostate Cancer Biomarker Identification: A Comparative Study, Wenjuan Jiang
Prostate Cancer Biomarker Identification: A Comparative Study, Wenjuan Jiang
Computational Modeling & Simulation Engineering Theses & Dissertations
With the current development of proteomics techniques, the discovery of potential molecular biomarkers for early detection of prostate cancer has been greatly improved. In this thesis, we implemented five classifiers including the support vector machine (SVM), Bayesian Classifier, Decision Tree, Random Forest and the Multilayer perceptron (MLP) to test their effectiveness in prostate cancer biomarker identification using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) tissue imaging data. The classifiers were utilized to discriminate cancer mass spectra from normal ones for the data collected at the Eastern Virginia Medical School (EVMS). There are 94 7 spectra from normal tissues and 27 from …
Automatic Diagnosis For Prostate Cancer Using Run-Length Matrix Method, Xiaoyan Sun, Shao-Hui Chuang, Jiang Li, Frederic Mckenzie, Nico Karssemeijer (Ed.), Maryellen L. Giger (Ed.)
Automatic Diagnosis For Prostate Cancer Using Run-Length Matrix Method, Xiaoyan Sun, Shao-Hui Chuang, Jiang Li, Frederic Mckenzie, Nico Karssemeijer (Ed.), Maryellen L. Giger (Ed.)
Electrical & Computer Engineering Faculty Publications
Prostate cancer is the most common type of cancer and the second leading cause of cancer death among men in US1. Quantitative assessment of prostate histology provides potential automatic classification of prostate lesions and prediction of response to therapy. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. In this application, we utilize a texture analysis method based on the run-length matrix for identifying tissue abnormalities in prostate histology. A tissue sample was collected from a radical prostatectomy, H&E fixed, and assessed by a pathologist …