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Computer-Aided Image Analysis and Detection of Prostate Cancer: Using Immunostaining for Alpha-Methylacyl-CoA Racemase, p63, and High-Molecular-Weight Cytokeratin

Computer-Aided Image Analysis and Detection of Prostate Cancer: Using Immunostaining for Alpha-Methylacyl-CoA Racemase, p63, and High-Molecular-Weight Cytokeratin

Yahui Peng, Yulei Jiang, Ximing J. Yang
Copyright: © 2012 |Pages: 19
ISBN13: 9781466600591|ISBN10: 1466600594|EISBN13: 9781466600607
DOI: 10.4018/978-1-4666-0059-1.ch012
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MLA

Peng, Yahui, et al. "Computer-Aided Image Analysis and Detection of Prostate Cancer: Using Immunostaining for Alpha-Methylacyl-CoA Racemase, p63, and High-Molecular-Weight Cytokeratin." Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, edited by Kenji Suzuki, IGI Global, 2012, pp. 238-256. https://doi.org/10.4018/978-1-4666-0059-1.ch012

APA

Peng, Y., Jiang, Y., & Yang, X. J. (2012). Computer-Aided Image Analysis and Detection of Prostate Cancer: Using Immunostaining for Alpha-Methylacyl-CoA Racemase, p63, and High-Molecular-Weight Cytokeratin. In K. Suzuki (Ed.), Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis (pp. 238-256). IGI Global. https://doi.org/10.4018/978-1-4666-0059-1.ch012

Chicago

Peng, Yahui, Yulei Jiang, and Ximing J. Yang. "Computer-Aided Image Analysis and Detection of Prostate Cancer: Using Immunostaining for Alpha-Methylacyl-CoA Racemase, p63, and High-Molecular-Weight Cytokeratin." In Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, edited by Kenji Suzuki, 238-256. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0059-1.ch012

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Abstract

Immunohistochemistry (IHC) is an adjunct tool for clinical histologic diagnosis of diseases. A common IHC technique for prostate cancer diagnosis is a triple-antibody cocktail with Alpha-Methylacyl-CoA Racemase (AMACR), p63, and High-Molecular-Weight Cytokeratin (HMWCK), which stains certain types of cells into two distinct colors. The authors have developed an automated computer technique that detects prostate cancer in prostate tissue sections processed with the triple-antibody cocktail. Test and validation of the authors’ technique on digital images obtained from conventional microscopes (region of interest images) showed that the computer technique can recognize prostatic adenocarcinoma with both high sensitivity and high specificity. The authors also used this computer technique to analyze whole-slide images of prostate biopsy and the initial results are promising. With further development and refinement, this computer technique could become a useful tool for pathologists to detect prostate cancer foci in histologic sections of tissue processed with the triple-antibody cocktail.

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