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

Shao Hui Chuang, Jiang Li, Xiaoyan Sun, Ayyappa Vadlamudi, Bo Sun, Lisa Cazares, Julius Nyalwidhe, Dean Troyer, John Semmes, Frederic D. McKenzie

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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%).

Original languageEnglish (US)
Pages (from-to)1247-1259
Number of pages13
Issue number10
StatePublished - Oct 2012
Externally publishedYes


  • biomarker identification
  • imaging biomarker
  • MALDI mass spectra
  • prostate cancer

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Graphics and Computer-Aided Design


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