Mass spectrometry-based proteomic pattern analysis for prostate cancer detection using neural networks with statistical significance test-based feature selection

Mass spectrometry-based proteomics provides a promising approach for accurate diagnosis of different diseases. However, there are some problems in the mass spectral data such as huge volume, data complexity and the presence of noise. These problems make analyzing the proteomic pattern difficult. In this paper, a neural network-based system is proposed for proteomic pattern analysis for prostate cancer screening. The system consists of three stages: feature selection based on statistical significant test, classification by a Radial Basis Function Neural Network (RBFNN) and a probabilistic neural network (PNN), and finally results optimization through ROC analysis. The experimental results show that the proposed system's performance is excellent in comparison with the existing tools. The high sensitivity (97.1%) and specificity (96.8%) of the proposed system when combined with prostatic biopsy are expected to help in early detection of prostate cancer.