OBJECTIVE: The purpose of the study was to assess the predictive value for prostate cancer of MRI using morphologic (T2-weighted imaging [T2WI]) and functional (MR spectroscopy [MRS], diffusion-weighted imaging [DWI], and dynamic contrast-enhanced [DCE] MRI) sequences and the free-to-total prostate-specific antigen (PSA) ratio, alone and combined. MATERIALS AND METHODS: This retrospective study included 70 patients (PSA level, > 4 ng/mL; free-to-total PSA ratio, < 20%) who underwent endorectal 1.5-T MRI before biopsy. We graded the likelihood of cancer on a 5-point scale. Imaging data were compared with histologic results on biopsy or prostatectomy. Accuracies were estimated from the area under receiver operating characteristic using the hemiprostate as the unit of analysis. A p value less than 0.05 denoted statistical significance. RESULTS: The model combining all variables was more accurate than each variable alone (95.2% vs 73.5% for T2WI, 76.0% for MRS, 81.8% for DWI, 75.6% for DCE-MRI, and 78.8% for free-to-total PSA ratio). The complete model had accuracy similar to that of combining two imaging variables with free-to-total PSA ratio, especially free-to-total PSA ratio, T2WI, and DWI (94.0%); and free-to-total PSA ratio, DWI, and MRS (93.8%); with negative predictive values of 91.0% and 89.5%, respectively. The best models combining two imaging variables (MRS and DWI, 85.8%; T2WI and DWI, 84.8%) had accuracy that was similar to that of the combination of all imaging variables (87.3%) and higher than that of the best individual imaging variable (DWI, 81.8%), but lower than that of the complete model. CONCLUSION: The combination of at least one functional technique with free-to-total PSA ratio is more accurate than combining only imaging variables in cancer detection. The use of more than two imaging variables does not increase the detection rate. Functional MRI has the potential to help avoid a large number of negative biopsies.
OBJECTIVE: The purpose of the study was to assess the predictive value for prostate cancer of MRI using morphologic (T2-weighted imaging [T2WI]) and functional (MR spectroscopy [MRS], diffusion-weighted imaging [DWI], and dynamic contrast-enhanced [DCE] MRI) sequences and the free-to-total prostate-specific antigen (PSA) ratio, alone and combined. MATERIALS AND METHODS: This retrospective study included 70 patients (PSA level, > 4 ng/mL; free-to-total PSA ratio, < 20%) who underwent endorectal 1.5-T MRI before biopsy. We graded the likelihood of cancer on a 5-point scale. Imaging data were compared with histologic results on biopsy or prostatectomy. Accuracies were estimated from the area under receiver operating characteristic using the hemiprostate as the unit of analysis. A p value less than 0.05 denoted statistical significance. RESULTS: The model combining all variables was more accurate than each variable alone (95.2% vs 73.5% for T2WI, 76.0% for MRS, 81.8% for DWI, 75.6% for DCE-MRI, and 78.8% for free-to-total PSA ratio). The complete model had accuracy similar to that of combining two imaging variables with free-to-total PSA ratio, especially free-to-total PSA ratio, T2WI, and DWI (94.0%); and free-to-total PSA ratio, DWI, and MRS (93.8%); with negative predictive values of 91.0% and 89.5%, respectively. The best models combining two imaging variables (MRS and DWI, 85.8%; T2WI and DWI, 84.8%) had accuracy that was similar to that of the combination of all imaging variables (87.3%) and higher than that of the best individual imaging variable (DWI, 81.8%), but lower than that of the complete model. CONCLUSION: The combination of at least one functional technique with free-to-total PSA ratio is more accurate than combining only imaging variables in cancer detection. The use of more than two imaging variables does not increase the detection rate. Functional MRI has the potential to help avoid a large number of negative biopsies.
Authors: Andrew B Rosenkrantz; Hersh Chandarana; Nicole Hindman; Fang-Ming Deng; James S Babb; Samir S Taneja; Christian Geppert Journal: Eur Radiol Date: 2013-06-12 Impact factor: 5.315
Authors: Daniel N Costa; B Nicolas Bloch; David F Yao; Martin G Sanda; Long Ngo; Elizabeth M Genega; Ivan Pedrosa; William C DeWolf; Neil M Rofsky Journal: Magn Reson Imaging Date: 2013-04-18 Impact factor: 2.546