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Proceedings Paper

Prostate cancer region prediction using MALDI mass spectra
Author(s): Ayyappa Vadlamudi; Shao-Hui Chuang; Xiaoyan Sun; Lisa Cazares; Julius Nyalwidhe; Dean Troyer; O. John Semmes; Jiang Li; Frederic D. McKenzie
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Paper Abstract

For the early detection of prostate cancer, the analysis of the Prostate-specific antigen (PSA) in serum is currently the most popular approach. However, previous studies show that 15% of men have prostate cancer even their PSA concentrations are low. MALDI Mass Spectrometry (MS) proves to be a better technology to discover molecular tools for early cancer detection. The molecular tools or peptides are termed as biomarkers. Using MALDI MS data from prostate tissue samples, prostate cancer biomarkers can be identified by searching for molecular or molecular combination that can differentiate cancer tissue regions from normal ones. Cancer tissue regions are usually identified by pathologists after examining H&E stained histological microscopy images. Unfortunately, histopathological examination is currently done on an adjacent slice because the H&E staining process will change tissue's protein structure and it will derogate MALDI analysis if the same tissue is used, while the MALDI imaging process will destroy the tissue slice so that it is no longer available for histopathological exam. For this reason, only the most confident cancer region resulting from the histopathological examination on an adjacent slice will be used to guide the biomarker identification. It is obvious that a better cancer boundary delimitation on the MALDI imaging slice would be beneficial. In this paper, we proposed methods to predict the true cancer boundary, using the MALDI MS data, from the most confident cancer region given by pathologists on an adjacent slice.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 762422 (9 March 2010); doi: 10.1117/12.844494
Show Author Affiliations
Ayyappa Vadlamudi, Old Dominion Univ. (United States)
Shao-Hui Chuang, Old Dominion Univ. (United States)
Xiaoyan Sun, Old Dominion Univ. (United States)
Lisa Cazares, Eastern Virginia Medical School (United States)
Julius Nyalwidhe, Eastern Virginia Medical School (United States)
Dean Troyer, Eastern Virginia Medical School (United States)
O. John Semmes, Eastern Virginia Medical School (United States)
Jiang Li, Old Dominion Univ. (United States)
Frederic D. McKenzie, Old Dominion Univ. (United States)


Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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