Prostate cancer has a high prevalence and mortality, being the most diagnosed urologic cancer. Prostatic magnetic resonance imaging showed high sensitivity in the detection of clinically significant neoplasia and agreement with the Gleason score. Therefore, we attempted to evaluate the diagnostic accuracy of the prostate imaging reporting and data system, using biopsy and prostatectomy as the reference standard. The secondary goal of correlating prostatic magnetic resonance imaging findings and anatomopathological samples is obtained.
We retrospectively analyzed seventy-nine 1.5 Tesla prostatic magnetic resonance imaging scans in patients aged 31 to 86 years, performed at the Clinical Hospital of the Federal University of Paraná between January 2015 and February 2018.
Considering all 79 patients, prostatic magnetic resonance imaging was able to diagnose tumor in 47 patients (59.4%). Considering the peripheral zone, the prostatic magnetic resonance imaging had a sensitivity of 75.0% (95% confidence interval: 52.1%-98.0%), specificity of 89.5% (95% confidence interval: 66.0%-100%), 94.4% positive predictive value (95% confidence interval: 71.0%-100%), 66.7% negative predictive value (95% confidence interval: 43.0%-69.0%), 83.8% Positive Likelihood Ratio (PVR) (95% confidence interval: 60.0%-100%), 27.9% Negative Likelihood Ratio (RVN) (95% confidence interval: 5.0%-50.0 %), and accuracy of 86.3% (95% confidence interval: 63.0%-100%). The receiver operating characteristic curve obtained demonstrated the sensitivity variation according to the prostate imaging reporting and data system score of the patients, obtaining an area under the curve of 84.8 for a prostate imaging reporting and data system cutoff of 3.
The use of the prostate imaging reporting and data system score is useful for the screening and classification of prostate cancer, due to its easy reproducibility, even in a population with an unknown prostate cancer prevalence, which can be easily correlated with biopsy studies and/or radical prostatectomy.
Urologia. 2019 Jul 14 [Epub ahead of print]
Patricia Yokoo, Gabriel Lucca de Oliveira Salvador, Jesus José André Quintana Castillo, Ana Carolina Nicoletti Basso, Rafael Sarmento do Amaral, Rafael Olinto Pelaez de Campos, Renan Arrais Ykeda Barreto, Oscar Fernando Ghattas Orozco, Alexandre Cavalheiro Cavalli, Mauricio Zaparolli
1 Department of Radiology, Internal Medicine Branch, Hospital de Clinicas, Federal University of Paraná, Curitiba, Brazil., 2 Department of Pathology, Diagnostic Aid Branch, Hospital de Clinicas, Federal University of Paraná, Curitiba, Brazil., 3 Department of Urology, Surgery Branch, Hospital de Clinicas, Federal University of Paraná, Curitiba, Brazil.