Validation of the Dominant Sequence Paradigm and Role of Dynamic Contrast-enhanced Imaging in PI-RADS Version 2

Abstract

Purpose To validate the dominant pulse sequence paradigm and limited role of dynamic contrast material-enhanced magnetic resonance (MR) imaging in the Prostate Imaging Reporting and Data System (PI-RADS) version 2 for prostate multiparametric MR imaging by using data from a multireader study. Materials and Methods This HIPAA-compliant retrospective interpretation of prospectively acquired data was approved by the local ethics committee. Patients were treatment-naïve with endorectal coil 3-T multiparametric MR imaging. A total of 163 patients were evaluated, 110 with prostatectomy after multiparametric MR imaging and 53 with negative multiparametric MR imaging and systematic biopsy findings. Nine radiologists participated in this study and interpreted images in 58 patients, on average (range, 56-60 patients). Lesions were detected with PI-RADS version 2 and were compared with whole-mount prostatectomy findings. Probability of cancer detection for overall, T2-weighted, and diffusion-weighted (DW) imaging PI-RADS scores was calculated in the peripheral zone (PZ) and transition zone (TZ) by using generalized estimating equations. To determine dominant pulse sequence and benefit of dynamic contrast-enhanced (DCE) imaging, odds ratios (ORs) were calculated as the ratio of odds of cancer of two consecutive scores by logistic regression. Results A total of 654 lesions (420 in the PZ) were detected. The probability of cancer detection for PI-RADS category 2, 3, 4, and 5 lesions was 15.7%, 33.1%, 70.5%, and 90.7%, respectively. DW imaging outperformed T2-weighted imaging in the PZ (OR, 3.49 vs 2.45; P = .008). T2-weighted imaging performed better but did not clearly outperform DW imaging in the TZ (OR, 4.79 vs 3.77; P = .494). Lesions classified as PI-RADS category 3 at DW MR imaging and as positive at DCE imaging in the PZ showed a higher probability of cancer detection than did DCE-negative PI-RADS category 3 lesions (67.8% vs 40.0%, P = .02). The addition of DCE imaging to DW imaging in the PZ was beneficial (OR, 2.0; P = .027), with an increase in the probability of cancer detection of 15.7%, 16.0%, and 9.2% for PI-RADS category 2, 3, and 4 lesions, respectively. Conclusion DW imaging outperforms T2-weighted imaging in the PZ; T2-weighted imaging did not show a significant difference when compared with DW imaging in the TZ by PI-RADS version 2 criteria. The addition of DCE imaging to DW imaging scores in the PZ yields meaningful improvements in probability of cancer detection. ^© RSNA, 2017 An earlier incorrect version of this article appeared online. This article was corrected on July 27, 2017. Online supplemental material is available for this article.

Figure 1:

Flow diagram shows inclusion and exclusion criteria. mpMRI = multiparametric MR imaging.

Figure 2:

Left: Screenshots for nine readers. Images were obtained in a 66-year-old man with a prostate-specific antigen level of 8.3 ng/mL who underwent endorectal coil 3-T multiparametric MR imaging with T2-weighted (apparent diffusion coefficient, b value = 2000 sec/mm²) and DCE MR imaging followed by prostatectomy. Nine readers were asked to detect all lesions that would be included in a clinical report and score them with PI-RADS version 2. Shown are the T2-weighted screen shots of all nine readers who marked the largest lesion diameter of an anterior midtransition zone lesion that received a Gleason score of 3 + 4 at prostatectomy. Right: Histopathologic image.

Figure 3:

Graph shows probability of cancer detection with standard error for each PI-RADS version 2 score. Each score showed an added benefit over the previous score (P < .05). DW imaging lesions with a PI-RADS score of 3 and DCE-positive lesions in the PZ (Gleason 3 + 1) represent a distinct risk population from all other PI-RADS category 4 or 3 lesions. * = P < .05. CDR = cancer detection rate.

Figure 4:

Graph shows validation of the dominant parameter in the PZ. PI-RADS scores at DW and T2-weighted imaging in the PZ are shown with corresponding ORs and P value for goodness of fit. PI-RADS DW imaging scores showed a higher predictive value for high likelihood scores (PI-RADS category 4 or 5) and a lower predictive value for low likelihood scores (PI-RADS category 2) versus PI-RADS T2-weighted scores (OR = 3.49 vs 2.35, P = .008).

Figure 5:

Graph shows validation of the dominant parameter in the TZ. PI-RADS scores at DW and T2-weighted imaging in the TZ are shown with corresponding ORs and P value for goodness of fit. No clear separation in incremental prediction value of PI-RADS scores was seen (OR, 3.77 vs 4.79; P = .494). However, the observed versus predicted plot trended for a better fit for T2-weighted (goodness-of-fit test, P = .521) over DW (goodness-of-fit test, P = .102) imaging.

Figure 6:

Graph shows incremental value of DCE in the PZ to DW imaging scoring for all lesions. Displayed are the observed cancer detection rate (CDR) for each PI-RADS DW imaging score in the PZ, with DCE negativity or positivity and PI-RADS DW imaging score as independent variables in the model-based prediction with 95% CI bars. Total number of lesions detected for a given PI-RADS score is shown in red below each score. DCE positivity had an OR of 2.0 (95% CI: 1.08, 3.70; P = .027) for cancer positivity.

Figure 7:

Graph shows incremental value of DCE in the TZ to T2-weighted imaging for all lesions. Displayed are the observed cancer detection rate (CDR) for each PI-RADS T2-weighted imaging score in the TZ, with DCE negativity or positivity and PI-RADS T2-weighted scores as independent variables in the model-based prediction with 95% CI bars. Total number of lesions detected for a given PI-RADS score is shown in red below each score. DCE positivity had an OR of 2.8 (95% CI 1.09, 7.16; P = .032) for cancer positivity. However, the model is limited by fit to the observed values (P = .009).