Document Type

Conference Paper

Publication Date

2010

DOI

10.1117/12.844403

Publication Title

Medical Imaging 2010: Computer-Aided Diagnosis; Proceedings of SPIE

Volume

7624

Pages

762433 (9 pp.)

Abstract

Prostate cancer is the second most common type of cancer among men in US [1]. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates. Unfortunately, traditional processing methods require histopathological examination on one slice of a biopsy sample while the adjacent slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest confidence tumor regions gained from the histopathological analysis are then mapped to the MALDI spectra data to estimate the regions for biomarker identification from the MALDI imaging. This paper describes a process to provide a significantly better estimate of the cancer tumor to be mapped onto the MALDI imaging spectra coordinates using the high confidence region to predict the true area of the tumor on the adjacent MALDI imaged slice.

Rights

© 2010 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.

Original Publication Citation

Chuang, S.-H., Sun, X., Cazares, L., Nyalwidhe, J., Troyer, D., Semmes, O. J., Li, J., & McKenzie, F. (2010) Adjacent slice prostate cancer prediction to inform MALDI imaging biomarker analysis. In Nico Karssemeijer & Ronald M. Summers (Eds.), Medical Imaging 2010: Computer-Aided Diagnosis, Proceedings of SPIE Vol. 7624 (762433). SPIE. https://doi.org/10.1117/12.844403

ORCID

0000-0003-0091-6986 (Li)

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