Faculty Bibliography

OBJECTIVE. The purpose of this article is to evaluate the utility of various morphologic and quantitative MRI features in differentiating central renal cell carcinoma (RCC) from renal pelvic urothelial carcinoma. MATERIALS AND METHODS. Sixty patients (39 men and 21 women; mean [+/- SD] age, 65 +/- 14 years; 48 with central RCC and 12 with renal pelvic urothelial carcinoma) who underwent MRI, including diffusion-weighted imaging (b values, 0, 400, and 800 s/mm(2)) and dynamic contrast-enhanced imaging, before histopathologic confirmation were included. Tumor T2 signal intensity and apparent diffusion coefficients (ADCs) were measured and normalized to muscle and CSF (hereafter referred to as normalized T2 signal and normalized ADC, respectively) and then were compared using receiver operating characteristic analysis. Also, two blinded radiologists independently assessed all tumors for various qualitative features, which were compared with the Fisher exact test and unpaired Student t test. RESULTS. Urothelial carcinoma exhibited significantly lower normalized ADC than did RCC (p = 0.008), but no significant difference was seen in ADC or normalized T2 signal intensity (p = 0.247-0.773). Normalized ADC had the highest area under the curve (0.757); normalized ADC below an optimal threshold of 0.451 was associated with sensitivity of 83% and specificity of 71% for diagnosing urothelial carcinoma. Features that were significantly more prevalent in urothelial carcinoma included global impression of urothelial carcinoma, location centered within the collecting system, collecting system defect, extension to the ureteropelvic junction, preserved renal shape, absence of cystic or necrotic areas, absence of hemorrhage, homogeneous enhancement, and hypovascularity (all p < 0.033). Increased T1 signal intensity suggestive of hemorrhage was significantly more prevalent in RCC (p = 0.02). Interreader agreement for the subjective features ranged from 61.7% to 98.3%. CONCLUSION. In addition to various qualitative MRI parameters, normalized ADC has utility in differentiating central RCC from renal pelvic urothelial carcinoma. Such differentiation may assist decisions regarding possible biopsy and treatment planning.

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.

AIM: To assess a novel method of three-dimensional (3D) co-registration of prostate cancer digital histology and in-vivo multiparametric magnetic resonance imaging (mpMRI) image sets for clinical usefulness. MATERIAL AND METHODS: A software platform was developed to achieve 3D co-registration. This software was prospectively applied to three patients who underwent radical prostatectomy. Data comprised in-vivo mpMRI [T2-weighted, dynamic contrast-enhanced weighted images (DCE); apparent diffusion coefficient (ADC)], ex-vivo T2-weighted imaging, 3D-rebuilt pathological specimen, and digital histology. Internal landmarks from zonal anatomy served as reference points for assessing co-registration accuracy and precision. RESULTS: Applying a method of deformable transformation based on 22 internal landmarks, a 1.6 mm accuracy was reached to align T2-weighted images and the 3D-rebuilt pathological specimen, an improvement over rigid transformation of 32% (p = 0.003). The 22 zonal anatomy landmarks were more accurately mapped using deformable transformation than rigid transformation (p = 0.0008). An automatic method based on mutual information, enabled automation of the process and to include perfusion and diffusion MRI images. Evaluation of co-registration accuracy using the volume overlap index (Dice index) met clinically relevant requirements, ranging from 0.81-0.96 for sequences tested. Ex-vivo images of the specimen did not significantly improve co-registration accuracy. CONCLUSION: This preliminary analysis suggests that deformable transformation based on zonal anatomy landmarks is accurate in the co-registration of mpMRI and histology. Including diffusion and perfusion sequences in the same 3D space as histology is essential further clinical information. The ability to localize cancer in 3D space may improve targeting for image-guided biopsy, focal therapy, and disease quantification in surveillance protocols.

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.

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.