Faculty Bibliography

Purpose: To compare the recently proposed Prostate Imaging Reporting and Data System (PI-RADS) scale that incorporates fixed criteria and a standard Likert scale based on overall impression in prostate cancer localization using multiparametric magnetic resonance (MR) imaging. Materials and Methods: This retrospective study was HIPAA compliant and institutional review board approved. Seventy patients who underwent 3-T pelvic MR imaging, including T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast material-enhanced imaging, with a pelvic phased-array coil before radical prostatectomy were included. Three radiologists, each with 6 years of experience, independently scored 18 regions (12 peripheral zone [PZ], six transition zone [TZ]) using PI-RADS (range, scores 3-15) and Likert (range, scores 1-5) scales. Logistic regression for correlated data was used to compare scales for detection of tumors larger than 3 mm in maximal diameter at prostatectomy. Results: Maximal accuracy was achieved with score thresholds of 8 and higher and of 3 and higher for PI-RADS and Likert scales, respectively. At these thresholds, in the PZ, similar accuracy was achieved with the PI-RADS scale and the Likert scale for radiologist 1 (89.0% vs 88.2%, P = .223) and radiologist 3 (88.5% vs 88.2%, P = .739) and greater accuracy was achieved with the PI-RADS scale than the Likert scale for radiologist 2 (89.6% vs 87.1%, P = .008). In the TZ, accuracy was lower with the PI-RADS scale than with the Likert scale for radiologist 1 (70.0% vs 87.1%, P < .001), radiologist 2 (87.6% vs 92.6%, P = .002), and radiologist 3 (82.9% vs 91.2%, P < .001). For tumors with Gleason score of at least 7, sensitivity was higher with the PI-RADS scale than with the Likert scale for radiologist 1 (88.6% vs 82.6%, P = .032), and sensitivity was similar for radiologist 2 (78.0% vs 76.5, P = .467) and radiologist 3 (77.3% vs 81.1%, P = .125). Conclusion: Radiologists performed well with both PI-RADS and Likert scales for tumor localization, although, in the TZ, performance was better with the Likert scale than the PI-RADS scale. (c) RSNA, 2013 Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13122233/-/DC1.

OBJECTIVE: To evaluate the role of multiparametric magnetic resonance imaging (MRI) performed in men without a biopsy-proven diagnosis of prostate cancer using follow-up biopsy as the reference standard. MATERIALS AND METHODS: Forty-two patients without biopsy-proven cancer and who underwent MRI were included. In all patients, MRI was performed at 3T using a pelvic phased-array coil and included T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced imaging. Thirteen had undergone no previous biopsy, and 29 had undergone at least 1 previous negative biopsy. All patients underwent prostate biopsy following MRI. Two fellowship-trained radiologists in consensus reviewed all cases and categorized each lobe as positive or negative for tumor. These interpretations were correlated with findings on post-MRI biopsy. RESULTS: Follow-up biopsy was positive in 23 lobes in 15 patients (36% of study cohort). On a per-patient basis, MRI had a sensitivity of 100%, specificity of 74%, positive predictive value (PPV) of 68%, and negative predictive value (NPV) of 100%. On a per-lobe basis, MRI had a sensitivity of 65%, specificity of 84%, PPV of 60%, and NPV of 86%. There was a nearly significant association between Gleason score and tumor detection on MRI (P = 0.072). CONCLUSIONS: In our sample, MRI had 100% sensitivity in predicting the presence of tumor on subsequent biopsy on a per-patient basis, suggesting a possible role for MRI in selecting patients with an elevated prostatic specific antigen (PSA) to undergo prostate biopsy. However, MRI had weaker specificity for prediction of a subsequent positive biopsy, as well as weaker sensitivity for tumor on a per-lobe basis, indicating that in patients with a positive MRI result, tissue sampling remains necessary for confirmation of the diagnosis as well as for treatment planning.

OBJECTIVES: To investigate the impact of prostate computed diffusion-weighted imaging (DWI) on image quality and tumour detection. METHODS: Forty-nine patients underwent 3-T magnetic resonance imaging using a pelvic phased-array coil before prostatectomy, including DWI with b values of 50 and 1,000 s/mm(2). Computed DW images with b value 1,500 s/mm(2) were generated from the lower b-value images. Directly acquired b-1,500 DW images were obtained in 39 patients. Two radiologists independently assessed DWI for image quality measures and location of the dominant lesion. A third radiologist measured tumour-to-peripheral-zone (PZ) contrast. Pathological findings from prostatectomy served as the reference standard. RESULTS: Direct and computed b-1,500 DWI showed better suppression of benign prostate tissue than direct b-1,000 DWI for both readers (P /= 0.180). Tumour-to-PZ contrast was greater on computed b-1,500 than on either direct DWI set (P < 0.001). CONCLUSION: Computed DWI of the prostate using b value >/=1,000 s/mm(2) improves image quality and tumour detection compared with acquired standard b-value images. KEY POINTS: * Diffusion weighted MRI is increasingly used for diagnosing and assessing prostate carcinoma. * Prostate computed DWI can extrapolate high b-value images from lower b values. * Computed DWI provides greater suppression of benign tissue than lower b-value images. * Computed DWI provides less distortion and artefacts than images using same b value. * Computed DWI provides better diagnostic performance than lower b-value images.

PURPOSE: The purpose of this study was to compare the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging for prostate cancer (PCa) detection by performing a meta-analysis of studies evaluating these techniques within the same patient cohort. METHODS: Evidence-based online databases were searched for studies reporting the performance of DWI and DCE in PCa detection in the same patient cohorts using histopathology as reference standard and providing sufficient data to construct 2 x 2 contingency tables. Pooled estimates of diagnostic performance were computed across included studies. RESULTS: Of 80 initial studies identified, 5 studies (total of 265 patients and 1730 prostatic regions) met criteria for inclusion in the meta-analysis. Pooled sensitivity was 58.4% (95% confidence interval [CI], 53.5%-63.1%) for DWI and 55.3% (95% CI, 50.4%-60.1%) for DCE. Pooled specificity was 89.0% (95% CI, 87.2%-0.7%) for DWI and 87.9% (95% CI, 86.0%-89.6%) for DCE. At summary receiver-operating-characteristic analysis, area-under-the-curve was 0.810 (0.059) for DWI and 0.786 (0.079) for DCE. Heterogeneity across studies was high for sensitivity and specificity [inconsistency index (I), >90%], although heterogeneity of specificity was substantially improved after excluding an outlier study in terms of diagnostic threshold (I = 0.0%-68.8%). Relative performance of DWI and DCE remained similar after this exclusion CONCLUSIONS: There was a paucity of studies comparing DWI and DCE in the same patient cohorts, and heterogeneity among these studies was substantial. Nevertheless, performance of DWI and DCE was similar across identified studies, with both techniques showing substantially better specificity than sensitivity. Larger studies with uniform methodology are warranted to further understand relative merits of the 2 techniques.