BACKGROUND: An accurate prediction of final pathological stage is essential for proper patient selection to determine who will experience maximum benefit from radical prostatectomy. In this context, the Partin tables represent one of the most widely used statistical prediction tools. PURPOSE: To compare the accuracy in predicting pathological stage in patients intended for radical prostatectomy between 3-Tesla (T) magnetic resonance imaging (MRI) and the Partin tables in a prospective trial. MATERIAL AND METHODS: 27 men with staging results from 3T MRI using a phased-array coil were compared in terms of staging accuracy with whole-mount-section histopathologic analyses as the standard of reference. Probabilities for pathological stages were estimated according to the Partin tables. Spearman rank correlation and discriminant analysis were calculated to assess relationships. RESULTS: Histopathological evaluation revealed organ-confined disease (OC) in 21 (77.8%) and extracapsular extension (ECE) in six (22.2%) men. Three-Tesla MRI staging was accurate in all patients with OC and in four out of the six men with ECE. Accuracy of local staging was 85.2% (23 of 27). Sensitivity was 66.7% (95% confidence interval [CI] 0.223-0.957) and specificity 100% (95% CI 0.839-1) for the detection of ECE. Findings of MRI and the Partin tables showed a Spearman rho of 0.780 vs. 0.254 for OC and 0.780 vs. 0.363 for ECE, respectively. Compared to the Partin tables, MRI revealed standardized canonical discriminant function coefficients of 0.992 (P<0.001) vs. 0.205 (P=0.432) for OC and 0.965 (P<0.001) vs. 0.329 (P=0.197) for ECE, respectively. CONCLUSION: 3T MRI showed a high accuracy for the staging of clinically localized prostate cancer, and it was significantly more accurate in predicting the final pathological stage than the Partin tables.
BACKGROUND: An accurate prediction of final pathological stage is essential for proper patient selection to determine who will experience maximum benefit from radical prostatectomy. In this context, the Partin tables represent one of the most widely used statistical prediction tools. PURPOSE: To compare the accuracy in predicting pathological stage in patients intended for radical prostatectomy between 3-Tesla (T) magnetic resonance imaging (MRI) and the Partin tables in a prospective trial. MATERIAL AND METHODS: 27 men with staging results from 3T MRI using a phased-array coil were compared in terms of staging accuracy with whole-mount-section histopathologic analyses as the standard of reference. Probabilities for pathological stages were estimated according to the Partin tables. Spearman rank correlation and discriminant analysis were calculated to assess relationships. RESULTS: Histopathological evaluation revealed organ-confined disease (OC) in 21 (77.8%) and extracapsular extension (ECE) in six (22.2%) men. Three-Tesla MRI staging was accurate in all patients with OC and in four out of the six men with ECE. Accuracy of local staging was 85.2% (23 of 27). Sensitivity was 66.7% (95% confidence interval [CI] 0.223-0.957) and specificity 100% (95% CI 0.839-1) for the detection of ECE. Findings of MRI and the Partin tables showed a Spearman rho of 0.780 vs. 0.254 for OC and 0.780 vs. 0.363 for ECE, respectively. Compared to the Partin tables, MRI revealed standardized canonical discriminant function coefficients of 0.992 (P<0.001) vs. 0.205 (P=0.432) for OC and 0.965 (P<0.001) vs. 0.329 (P=0.197) for ECE, respectively. CONCLUSION: 3T MRI showed a high accuracy for the staging of clinically localized prostate cancer, and it was significantly more accurate in predicting the final pathological stage than the Partin tables.
Authors: B Nicolas Bloch; Elizabeth M Genega; Daniel N Costa; Ivan Pedrosa; Martin P Smith; Herbert Y Kressel; Long Ngo; Martin G Sanda; William C Dewolf; Neil M Rofsky Journal: Eur Radiol Date: 2012-06-03 Impact factor: 5.315
Authors: Nandinee Fariah Haq; Piotr Kozlowski; Edward C Jones; Silvia D Chang; S Larry Goldenberg; Mehdi Moradi Journal: Comput Med Imaging Graph Date: 2014-07-05 Impact factor: 4.790
Authors: Rossano Girometti; Martina Pancot; Marco Andrea Signor; Martina Urbani; Luca Balestreri; Chiara Zuiani Journal: Radiol Med Date: 2018-05-12 Impact factor: 3.469
Authors: Mehdi Moradi; Septimiu E Salcudean; Silvia D Chang; Edward C Jones; Nicholas Buchan; Rowan G Casey; S Larry Goldenberg; Piotr Kozlowski Journal: J Magn Reson Imaging Date: 2012-01-20 Impact factor: 4.813