Anatomic Segmentation Improves Prostate Cancer Detection With Artificial Neural Networks Analysis of H-1 Magnetic Resonance Spectroscopic Imaging

Purpose To assess whether an artificial neural network (ANN) model is a useful tool for automatic detection of cancerous voxels in the prostate from H-1-MRSI datasets and whether the addition of information about anatomical segmentation improves the detection of cancer. Materials and MethodsThe Institutional Review Board approved this HIPAA-compliant study and waived informed consent. Eighteen men with prostate cancer (median age, 55 years; range, 36-71 years) who underwent endorectal MRI/MRSI before radical prostatectomy were included in this study. These patients had at least one cancer area on wholemount histopathological map and at least one matching MRSI voxel suspicious for cancer detected. Two ANN models for automatic classification of MRSI voxels in the prostate were implemented and compared: model 1, which used only spectra as input, and model 2, which used the spectra plus information from anatomical segmentation. The models were trained, tested and validated using spectra from voxels that the spectroscopist had designated as cancer and that were verified on histopathological maps. ResultsAt ROC analysis, model 2 (AUC = 0.968) provided significantly better (P = 0.03) classification of cancerous voxels than did model 1 (AUC = 0.949). ConclusionAutomatic analysis of prostate MRSI to detect cancer using ANN model is feasible. Application of anatomical segmentation from MRI as an additional input to ANN improves the accuracy of detecting cancerous voxels from MRSI.J. Magn. Reson. Imaging 2014;40:1414-1421.