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%).
All Science Journal Classification (ASJC) codes
- Modeling and Simulation
- Computer Graphics and Computer-Aided Design