Accuracy and agreement of PIRADSv2 for prostate cancer mpMRI: A multireader study

Abstract

Purpose Multiparametric MRI (mpMRI) improves the detection of clinically significant prostate cancer, but is limited by interobserver variation. The second version of theProstate Imaging Reporting and Data System (PIRADSv2) was recently proposed as a standard for interpreting mpMRI. To assess the performance and interobserver agreement of PIRADSv2 we performed a multi‐reader study with five radiologists of varying experience. Materials and Methods Five radiologists (n = 2 prostate dedicated, n = 3 general body) blinded to clinicopathologic results detected and scored lesions on prostate mpMRI using PIRADSv2. The endorectal coil 3 Tesla MRI included T2W, diffusion‐weighted imaging (apparent diffusion coefficient, b2000), and dynamic contrast enhancement. Thirty‐four consecutive patients were included. Results were correlated with radical prostatectomy whole‐mount histopathology produced with patient‐specific three‐dimensional molds. An index lesion was defined on pathology as the lesion with highest Gleason score or largest volume if equivalent grades. Average sensitivity and positive predictive values (PPVs) for all lesions and index lesions were determined using generalized estimating equations. Interobserver agreement was evaluated using index of specific agreement. Results Average sensitivity was 91% for detecting index lesions and 63% for all lesions across all readers. PPV was 85% for PIRADS ≥ 3 and 90% for PIRADS ≥ 4. Specialists performed better only for PIRADS ≥ 4 with sensitivity 90% versus 79% (P = 0.01) for index lesions. Index of specific agreement among readers was 93% for the detection of index lesions, 74% for the detection of all lesions, and 85% for scoring index lesions, and 58% for scoring all lesions. Conclusion By using PIRADSv2, general body radiologists and prostate specialists can detect high‐grade index prostate cancer lesions with high sensitivity and agreement. Level of Evidence: 1 J. Magn. Reson. Imaging 2017;45:579–585.