Prostate cancer region prediction by fusing results from MALDI spectra-processing and texture analysis

Authors:
Shao-Hui Chuang;Jiang Li;Xiaoyan Sun;Ayyappa Vadlamudi;Bo Sun;Lisa Cazares;Julius Nyalwidhe;Dean Troyer;John Semmes;Frederic D Mckenzie
Affiliations:
Department of Electrical and Computer Engineering, Old Dominion University, 231 Kaufman Hall, Norfolk, VA, USA;Department of Electrical and Computer Engineering, Old Dominion University, 231 Kaufman Hall, Norfolk, VA, USA;Department of Electrical and Computer Engineering, Old Dominion University, 231 Kaufman Hall, Norfolk, VA, USA;Department of Electrical and Computer Engineering, Old Dominion University, 231 Kaufman Hall, Norfolk, VA, USA;Department of Mathematics and Computer Science, Lincoln University of the Commonwealth of Pennsylvania, Lincoln University, PA, USA;Department of Microbiology and Molecular Cell Biology, Department of Pathology and Anatomy, Virginia Prostate Center, George L. Wright, Jr, Center for Biomedical Proteomics Eastern Virginia Medica ...;Department of Microbiology and Molecular Cell Biology, Department of Pathology and Anatomy, Virginia Prostate Center, George L. Wright, Jr, Center for Biomedical Proteomics Eastern Virginia Medica ...;Department of Microbiology and Molecular Cell Biology, Department of Pathology and Anatomy, Virginia Prostate Center, George L. Wright, Jr, Center for Biomedical Proteomics Eastern Virginia Medica ...;Department of Microbiology and Molecular Cell Biology, Department of Pathology and Anatomy, Virginia Prostate Center, George L. Wright, Jr, Center for Biomedical Proteomics Eastern Virginia Medica ...;Department of Modeling, Simulation and Visualization Engineering, Old Dominion University, Norfolk, VA, USA
Venue:
Simulation
Year:
2012

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Abstract

We present a three-step method to predict prostate cancer (PCa) regions on biopsy tissue samples based on high-confidence, low-resolution PCa regions marked by a pathologist. First, we will apply a texture-analysis technique on a high-magnification optical image to predict PCa regions on an adjacent tissue slice. Second, we will design a prediction model for the same purpose, using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) tissue-imaging data from the adjacent slice. Finally, we will fuse those two results to obtain the PCa regions that will assist MALDI imaging biomarker identification. Experiment results show that the texture analysis-based prediction is sensitive (87.45%) but less specific (75%), and the prediction based on the MALDI spectra data processing is not sensitive (50.98%) but supremely specific (100%). By combining these two results, an optimized prediction for PCa regions on the adjacent slice can be achieved (sensitivity: 80.39%, specificity: 93.09%).