Is Prostate Imaging Reporting and Data System Version 2 Sufficiently Discovering Clinically Significant Prostate Cancer? Per-Lesion Radiology-Pathology Correlation Study.

Abstract

The objective of our study was to evaluate the performance of multiparametric MRI with Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) for detecting prostate cancer (PCA) and clinically significant PCA through this per-lesion one-to-one correlation study between pathologically proven lesions and MRI-visible lesions. A total of 93 PCA lesions from 44 patients who underwent radical prostatectomy were included in this retrospective study. Two radiologists scored every visible lesion with a PI-RADSv2 score of 3, 4, or 5 in each patient's multiparametric MRI examination using PI-RADSv2. A per-lesion one-to-one correlation between MRI-visible lesions and pathologically confirmed PCA lesions was conducted during regular radiology-pathology meetings at our center. The detection rates of clinically significant PCA and the proportions of clinically significant PCAs from MRI-visible and MRI-invisible PCAs were calculated. The performance of PI-RADSv2 for detecting clinically significant PCA was evaluated using the positive predictive value (PPV), negative predictive value (NPV), and area under the ROC curve (AUC) value. Using a PI-RADSv2 score of 3, 4, or 5 as an MRI-visible lesion, 46.88% of clinically significant PCA lesions were detected. The PPV, NPV, and AUC were 96.77%, 45.16%, and 0.72, respectively. Tumor volume and secondary Gleason grade showed a statistically significant difference between MRI-visible and MRI-invisible clinically significant PCAs. Multiparametric MRI with PI-RADSv2 missed a considerable number of clinically significant PCA lesions in this per-lesion analysis, causing a relatively low NPV and diagnostic performance compared with previous per-patient studies. However, the high PPV indicates that multiparametric MRI with PI-RADSv2 may be useful for follow-up of active surveillance and planning focal therapy.