PURPOSE: To assess whether an artificial neural network (ANN) model is a useful tool for automatic detection of cancerous voxels in the prostate from (1)H-MRSI datasets and whether the addition of information about anatomical segmentation improves the detection of cancer. MATERIALS AND METHODS: The 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 whole-mount 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. RESULTS: At ROC analysis, model 2 (AUC = 0.968) provided significantly better (P = 0.03) classification of cancerous voxels than did model 1 (AUC = 0.949). CONCLUSION: Automatic 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.
PURPOSE: To assess whether an artificial neural network (ANN) model is a useful tool for automatic detection of cancerous voxels in the prostate from (1)H-MRSI datasets and whether the addition of information about anatomical segmentation improves the detection of cancer. MATERIALS AND METHODS: The 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 whole-mount 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. RESULTS: At ROC analysis, model 2 (AUC = 0.968) provided significantly better (P = 0.03) classification of cancerous voxels than did model 1 (AUC = 0.949). CONCLUSION: Automatic 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.
Authors: Thorsten H Ecke; Peter Bartel; Steffen Hallmann; Stefan Koch; Jürgen Ruttloff; Henning Cammann; Michael Lein; Mark Schrader; Kurt Miller; Carsten Stephan Journal: Urol Oncol Date: 2010-04-03 Impact factor: 3.498
Authors: Satish E Viswanath; Nicholas B Bloch; Jonathan C Chappelow; Robert Toth; Neil M Rofsky; Elizabeth M Genega; Robert E Lenkinski; Anant Madabhushi Journal: J Magn Reson Imaging Date: 2012-02-15 Impact factor: 4.813
Authors: Gregory J Metzger; Chaitanya Kalavagunta; Benjamin Spilseth; Patrick J Bolan; Xiufeng Li; Diane Hutter; Jung W Nam; Andrew D Johnson; Jonathan C Henriksen; Laura Moench; Badrinath Konety; Christopher A Warlick; Stephen C Schmechel; Joseph S Koopmeiners Journal: Radiology Date: 2016-01-29 Impact factor: 11.105