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Background and Objective: Prostate Imaging for Recurrence Reporting (PI-RR) was introduced in 2021 to standardize the interpretation and reporting of multiparametric magnetic resonance imaging (MRI) for prostate cancer following whole-gland treatment. The system scores image on a scale from 1 to 5 and has shown promising results in single-center studies. The aim of our systematic review and meta-analysis was to assess the diagnostic performance of the PI-RR system in predicting the likelihood of local recurrence after whole-gland treatment., Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for diagnostic test accuracy were followed. Relevant databases were searched up to December 2023. Primary studies met the eligibility criteria if they reported MRI diagnostic performance in prostate cancer recurrence using PI-RR. Diagnostic performance for MRI was assessed using two different cutoff points (≥3 or ≥4 for positivity according to the PI-RR system). A meta-analysis with a random-effects model was used to estimate pooled sensitivity and specificity values., Key Findings and Limitations: Sixteen articles were identified for full-text reading, of which six were considered eligible, involving a total of 467 patients. Using a cutoff of PI-RR ≥3 (4 studies) for recurrent disease, the sensitivity was 77.8% (95% confidence interval [CI] 69.9-84.1%) and the specificity was 80.2% (95% CI 58.2-92.2%). Using a cutoff of PI-RR ≥4 (4 studies), the sensitivity was 61.9% (95% CI 35.6-82.7%) and the specificity was 86.6% (95% CI 75.1-93.3%). Overall, the inter-rater agreement varied from fair to excellent., Conclusions and Clinical Implications: PI-RR is accurate in detecting local recurrence after whole-gland treatment for prostate cancer and shows fair-to-good to excellent inter-reader agreement. Overall, a PI-RR cutoff of ≥3 showed high sensitivity and specificity., Patient Summary: We reviewed studies that reported on how good MRI scans using a scoring system called PI-RR were in detecting recurrence of prostate cancer. We found that this system shows good performance, with fair to excellent agreement between different radiologists., (Copyright © 2024 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

The aim of this article is to review the technical and clinical considerations encountered with PI-RADS 3 lesions, which are equivocal for clinically significant Prostate Cancer (csPCa) with detection rates ranging between 10% and 35%. The number of PI-RADS 3 lesions reported vary according to several factors including MRI quality and radiologist training/expertise among the most influential. PI-RADS v.2.1 updated definitions for scores 2 and 3 in the PZ and scores 1 and 2 in the TZ is reviewed. The role of DWI role is highlighted in the assessment of the TZ with the possibility of upgrading score 2 lesions to score 3 based on DWI score. Given the increased utilization for prostate MRI, biparametric MRI can be considered as an alternative for low-risk patients where there is a need to rule out csPCa acknowledging this technique may increase the number of indeterminate cases going for biopsies. Management of patients with equivocal lesions at mpMRI and factors influencing biopsy decision process remain as an unmet need and additional studies using molecular/imaging markers as well as artificial intelligence tools are needed to further address their role in proper patient selection for biopsy., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The purpose of this article is to review clinical application of the Prostate Imaging for Recurrence Reporting (PI-RR) system. This system, released in 2021, represents international consensus-based guidelines for the acquisition, interpretation, and reporting of multiparametric MRI performed to detect locally recurrent prostate cancer after radiation therapy or radical prostatectomy. The system reduces variability through use of a standardized and structured reporting approach whereby the overall level of suspicion of recurrence is classified on a 5-point scale. The overall suspicion score is derived from 5-point scales for assessing DWI and dynamic contrast-enhanced (DCE) imaging. Separate scales for both DWI and DCE imaging are provided for evaluation after radiation therapy and after radical prostatectomy. These scales account for the relation between detected abnormalities and the location of the primary tumor on pretreatment imaging. T2-weighted imaging is also assessed on a 5-point scale and is useful for anatomic imaging but does not influence the overall score. Initial retrospective studies have shown promising results with respect to the reproducibility and accuracy of PI-RR in detecting locally recurrent tumor.

Objectives: To evaluate the T2-weighted (T2W) MRI calculator to differentiate adrenal pheochromocytoma from lipid-poor adrenal adenoma., Methods: Twenty-nine consecutive pheochromocytomas resected between 2010 and 2019 were compared to 23 consecutive lipid-poor adrenal adenomas. Three blinded radiologists (R1, R2, R3) subjectively evaluated T2W signal intensity and heterogeneity and extracted T2W signal intensity ratio (SIR) and entropy. These values were imputed into a quantitative and qualitative T2W adrenal MRI calculator (logistic regression model encompassing T2W SIR + entropy and subjective SI [relative to renal cortex] and heterogeneity) using a predefined threshold to differentiate metastases from adenoma and accuracy derived by a 2 × 2 table analysis., Results: Subjectively, pheochromocytomas were brighter (p < 0.001) and more heterogeneous (p < 0.001) for all three radiologists. Inter-observer agreement was fair-to-moderate for T2W signal intensity (K = 0.37-0.46) and fair for heterogeneity (K = 0.24-0.32). Pheochromocytoma had higher T2W-SI-ratio (p < 0.001) and entropy (p < 0.001) for all three readers. The quantitative calculator differentiated pheochromocytoma from adenoma with high sensitivity, specificity, and accuracy (100% [95% confidence intervals 88-100%], 87% [66-97%], and 94% [86-100%] R1; 93% [77-99%], 96% [78-100%], and 94% [88-100%] R2; 97% [82-100%], 96% [78-100%], and 96% [91-100% R3]). The qualitative calculator was specific with lower sensitivity and overall accuracy (48% [29-68%], 100% [85-100%], and 74% [65-83%] R1; 45% [26-64%], 100% [85-100%], and 72% [63-82%] R2; 59% [39-77%], 100% [85-100%], and 79% [70-88% R3])., Conclusions: T2W signal intensity and heterogeneity differ, subjectively and quantitatively, in pheochromocytoma compared to adenoma. Use of a quantitative T2W adrenal calculator which combines T2W signal intensity ratio and entropy was highly accurate to diagnose pheochromocytoma outperforming subjective analysis., Key Points: • Pheochromocytomas have higher T2-weighted signal intensity and are more heterogeneous compared to lipid-poor adrenal adenomas evaluated subjectively and quantitatively. • The quantitative T2-weighted adrenal MRI calculator, a logistic regression model combining T2-weighted signal intensity ratio and entropy, is highly accurate for diagnosis of pheochromocytoma. • The qualitative T2-weighed adrenal MRI calculator had high specificity but lower sensitivity and overall accuracy compared to quantitative assessment and agreement was only fair-to-moderate., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Purpose: To evaluate if machine learning (ML) of radiomic features extracted from apparent diffusion coefficient (ADC) and T2-weighted (T2W) MRI can predict prostate cancer (PCa) diagnosis in Prostate Imaging-Reporting and Data System (PI-RADS) version 2.1 category 3 lesions., Methods: This multi-institutional review board-approved retrospective case-control study evaluated 158 men with 160 PI-RADS category 3 lesions (79 peripheral zone, 81 transition zone) diagnosed at 3-Tesla MRI with histopathology diagnosis by MRI-TRUS-guided targeted biopsy. A blinded radiologist confirmed PI-RADS v2.1 score and segmented lesions on axial T2W and ADC images using 3D Slicer, extracting radiomic features with an open-source software (Pyradiomics). Diagnostic accuracy for (1) any PCa and (2) clinically significant (CS; International Society of Urogenital Pathology Grade Group ≥ 2) PCa was assessed using XGBoost with tenfold cross -validation., Results: From 160 PI-RADS 3 lesions, there were 50.0% (80/160) PCa, including 36.3% (29/80) CS-PCa (63.8% [51/80] ISUP 1, 23.8% [19/80] ISUP 2, 8.8% [7/80] ISUP 3, 3.8% [3/80] ISUP 4). The remaining 50.0% (80/160) lesions were benign. ML of all radiomic features from T2W and ADC achieved area under receiver operating characteristic curve (AUC) for diagnosis of (1) CS-PCa 0.547 (95% Confidence Intervals 0.510-0.584) for T2W and 0.684 (CI 0.652-0.715) for ADC and (2) any PCa 0.608 (CI 0.579-0.636) for T2W and 0.642 (CI 0.614-0.0.670) for ADC., Conclusion: Our results indicate ML of radiomic features extracted from T2W and ADC achieved at best moderate accuracy for determining which PI-RADS category 3 lesions represent PCa., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Accurate detection of transition zone (TZ) prostate cancer (PCa) on magnetic resonance imaging (MRI) remains challenging using clinical subjective assessment due to overlap between PCa and benign prostatic hyperplasia (BPH). The objective of this paper is to describe a deep-learning-based framework for fully automated detection of PCa in the TZ using T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images., Method: This was a single-center IRB-approved cross-sectional study of men undergoing 3T MRI on two systems. The dataset consisted of 196 patients (103 with and 93 without clinically significant [Grade Group 2 or higher] TZ PCa) to train and test our proposed methodology, with an additional 168 patients with peripheral zone PCa used only for training. We proposed an ensemble of classifiers in which multiple U-Net-based models are designed for prediction of TZ PCa location on ADC map MR images, with initial automated segmentation of the prostate to guide detection. We compared accuracy of ADC alone to T2W and combined ADC+T2W MRI for input images, and investigated improvements using ensembles over their constituent models with different methods of diversity in individual models by hyperparameter configuration, loss function and model architecture., Results: Our developed algorithm reported sensitivity and precision of 0.829 and 0.617 in 56 test cases containing 31 instances of TZ PCa and in 25 patients without clinically significant TZ tumors. Patient-wise classification accuracy had an area under receiver operator characteristic curve (AUROC) of 0.974. Single U-Net models using ADC alone (sensitivity 0.829, precision 0.534) outperformed assessment using T2W (sensitivity 0.086, precision 0.081) and assessment using combined ADC+T2W (sensitivity 0.687, precision 0.489). While the ensemble of U-Nets with varying hyperparameters demonstrated the highest performance, all ensembles improved PCa detection compared to individual models, with sensitivities and precisions close to the collective best of constituent models., Conclusion: We describe a deep-learning-based method for fully automated TZ PCa detection using ADC map MR images that outperformed assessment by T2W and ADC+T2W., (© 2021 American Association of Physicists in Medicine.)

Objective: To compare MR-imaging features in benign lipid-rich and lipid-poor adrenal adenomas., Materials and Methods: With institutional review board approval, we compared 23 consecutive lipid-poor adenomas (chemical shift [CS] signal intensity [SI] index < 16.5%) imaged with MRI to 29 consecutive lipid-rich adenomas (CS-SI index ≥ 16.5%) imaged during the same time period. A blinded radiologist measured T2-weighted (T2W) SI ratio (adrenal adenoma/psoas muscle), dynamic enhancement wash-in (WI) and wash-out (WO) indices, and T2W texture features. Two blinded Radiologists (R1/R2) assessed T2W-SI (relative to renal cortex) and T2W heterogeneity (using 5-Point Likert scales). Comparisons were performed between groups using independent t tests and Chi-square with Holm-Bonferroni correction., Results: There was no difference in age or gender between groups (p = 0.594, 0.051 respectively). Subjectively, all lipid-rich and lipid-poor adenomas were rated hypointense or isointense compared to renal cortex and T2W-SI did not differ between groups (p = 0.129, 0.124 for R1, R2). Agreement was substantial (Kappa = 0.67). There was no difference in T2W SI ratio (1.8 ± 0.9 [0.5-4.3] lipid rich versus 2.2 ± 1.0 [0.6-4.3] lipid poor, p = 0.139). Enhancement WI and WO did not differ comparing lipid-rich and lipid-poor adenomas (p = 0.759, 0.422 respectively). There was no difference comparing lipid-rich and lipid-poor adenomas T2W heterogeneity judged subjectively (p = 0.695, 0.139 for R1, R2; Kappa = 0.19) or by texture analysis (entropy, kurtosis, skewness; p = 0.134-0.191) with all adenomas except for one rated as mostly or completely homogeneous., Conclusions: There is no difference in T2W signal intensity, enhancement pattern or T2W heterogeneity judged subjectively or by quantitative texture analysis comparing lipid-poor and lipid-rich adrenal adenomas., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objectives: To evaluate the prevalence of prostate cancer (PCa) of two PI-RADS version (v) 2.1 transition zone (TZ) features (PI-RADS 1 ['nodule in nodule'] and 2 ['homogeneous mildly hypointense area between nodules'])., Methods: With an institutional review board approval, from a 5-year cohort between 2012 and 2017, we retrospectively identified 53 consecutive men with radical prostatectomy (RP) confirmed TZ tumors and MRI. Three blinded radiologists (R1/2/3) independently evaluated T2-weighted and diffusion-weighted imaging (DWI) using PI-RADS v2.1 for the presence of (1) 'nodule in nodule' (recording 'cystic change', inner nodule encapsulation, size, and DWI score) and (2) 'homogeneous mildly hypointense area between nodules' (also recording size and DWI score). MRI-RP maps established ground truth. Primary tumor was evaluated assessing PI-RADS v2.1 category, size, and presence of imaging variants., Results: R1/2/3 identified 26/18/22 'nodule in nodule' respectively with 7.7% (2/26; 95% confidence interval [95% CI]: 0.1-17.9%), 5.6% (1/18; 95% CI: 0.01-16.1%), and 4.5% (1/22; 95% CI: 0.01-13.3%) PCa (both Gleason score 3 + 4 = 7). Agreement was fair-to-substantial, kappa = 0.222-0.696. 'Cystic change', inner nodule absent/incomplete encapsulation and DWI score ≥ 4 for R1/R2/R2 were present in 80.8% (21/26), 46.2% (12/26), 7.7% (2/26); 94.4% (17/18), 33.3% (6/18), 5.6% (1/18); and 59.1% (13/22), 63.6% (14/22), 9.1% (2/22). Both PCa had inner nodule absent/incomplete encapsulation and DWI score ≥ 4. No other TZ tumors demonstrated 'nodule in nodule', nodule 'cystic change', or 'homogeneous mildly hypointense area between nodules'. R1/2/3 identified 5/6/13 'homogeneous mildly hypointense area between nodules' with zero PCa for any reader (upper bound 95% CI: 24.7-52.2%). Interobserver agreement was fair-to-substantial, kappa = 0.104-0.779., Conclusion: The proportion of cancers in PI-RADS v2.1 'nodule in nodule' was low (~5-8%) with zero cancers detected in 'homogeneous mildly hypointense area between nodules'. When 'nodule in nodule' inner nodule shows absent or incomplete encapsulation with marked restricted diffusion, PCa may be considered; however, this warrants further studies., Key Points: • The prevalence of clinically significant prostate cancers in PI-RADS v2.1 'nodule in nodule' was low (5-8%, 95% CI: 0.1-17.9%). • Clinically significant prostate cancer was only detected in the 'nodule in nodule' variant when the inner nodule showed absent or incomplete encapsulation ('atypical nodule') with marked restricted diffusion. • 'Homogeneous mildly hypointense area between nodules' is likely benign with no cancers identified in the current study, however, with a wide 95% CI due to low prevalence., (© 2021. European Society of Radiology.)

Purpose: To determine if pharmacokinetic modeling of DCE-MRI can diagnose CS-PCa in PI-RADS category 3 PZ lesions with subjective negative DCE-MRI., Materials and Methods: In the present IRB approved, bi-institutional, retrospective, case-control study, we identified 73 men with 73 PZ PI-RADS version 2.1 category 3 lesions with MRI-directed-TRUS-guided targeted biopsy yielding: 12 PZ CS-PCa (ISUP Grade Group 2; N = 9, ISUP 3; N = 3), 27 ISUP 1 PCa and 34 benign lesions. An expert blinded radiologist segmented lesions on ADC and DCE images; segmentations were overlayed onto pharmacokinetic DCE-MRI maps. Mean values were compared between groups using univariate analysis. Diagnostic accuracy was assessed by ROC., Results: There were no differences in age, PSA, PSAD or clinical stage between groups (p = 0.265-0.645). Mean and 10th percentile ADC did not differ comparing CS-PCa to ISUP 1 PCa and benign lesions (p = 0.376 and 0.598) but was lower comparing ISUP ≥ 1 PCa to benign lesions (p < 0.001). Mean Ktrans (p = 0.003), Ve (p = 0.003) but not Kep (p = 0.387) were higher in CS-PCa compared to ISUP 1 PCa and benign lesions. There were no differences in DCE-MRI metrics comparing ISUP ≥ 1 PCa and benign lesions (p > 0.05). AUC for diagnosis of CS-PCa using Ktrans and Ve were: 0.69 (95% CI 0.52-0.87) and 0.69 (0.49-0.88)., Conclusion: Pharmacokinetic modeling of DCE-MRI parameters in PI-RADS category 3 lesions with subjectively negative DCE-MRI show significant differences comparing CS-PCa to ISUP 1 PCa and benign lesions, in this study outperforming ADC. Studies are required to further evaluate these parameters to determine which patients should undergo targeted biopsy for PI-RADS 3 lesions., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Prostate MRI is reported in clinical practice using the Prostate Imaging and Data Reporting System (PI-RADS). PI-RADS aims to standardize, as much as possible, the acquisition, interpretation, reporting, and ultimately the performance of prostate MRI. PI-RADS relies upon mainly subjective analysis of MR imaging findings, with very few incorporated quantitative features. The shortcomings of PI-RADS are mainly: low-to-moderate interobserver agreement and modest accuracy for detection of clinically significant tumors in the transition zone. The use of a more quantitative analysis of prostate MR imaging findings is therefore of interest. Quantitative MR imaging features including: tumor size and volume, tumor length of capsular contact, tumor apparent diffusion coefficient (ADC) metrics, tumor T 1 and T 2 relaxation times, tumor shape, and texture analyses have all shown value for improving characterization of observations detected on prostate MRI and for differentiating between tumors by their pathological grade and stage. Quantitative analysis may therefore improve diagnostic accuracy for detection of cancer and could be a noninvasive means to predict patient prognosis and guide management. Since quantitative analysis of prostate MRI is less dependent on an individual users' assessment, it could also improve interobserver agreement. Semi- and fully automated analysis of quantitative (radiomic) MRI features using artificial neural networks represent the next step in quantitative prostate MRI and are now being actively studied. Validation, through high-quality multicenter studies assessing diagnostic accuracy for clinically significant prostate cancer detection, in the domain of quantitative prostate MRI is needed. This article reviews advances in quantitative prostate MRI, highlighting the strengths and limitations of existing and emerging techniques, as well as discussing opportunities and challenges for evaluation of prostate MRI in clinical practice when using quantitative assessment. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2., (© 2020 International Society for Magnetic Resonance in Medicine.)

Introduction: We aimed to determine if clinical and imaging features can stratify men at higher risk for clinically significant (CS, International Society of Urological Pathology [ISUP] grade group ≥2) prostate cancer (PCa) in equivocal Prostate Imaging and Data Reporting System (PI-RADS) category 3 lesions on magnetic resonance imaging (MRI)., Methods: Approved by the institutional review board, this retrospective study involved 184 men with 198 lesions who underwent 3T-MRI and MRI-directed transrectal ultrasound biopsy for PI-RADS 3 lesions. Men were evaluated including clinical stage, prostate-specific antigen density (PSAD), indication, and MRI lesion size. Diagnoses for all men and by indication (no cancer, any PCa, CSPCa) were compared using multivariate logistic regression, including stage, PSAD, and lesion size., Results: We found an overall PCa rate of 31.8% (63/198) and 10.1% (20/198) CSPCa (13 grade group 2, five group 3, and two group 4). Higher stage (p=0.001), PSAD (p=0.007), and lesion size (p=0.015) were associated with CSPCa, with no association between CSPCa and age, PSA, or prostate volume (p>0.05). PSAD modestly predicted CSPCa area under the curve (AUC) 0.66 (95% confidence interval [CI] 0.518-0.794) in all men and 0.64 (0.487-0.799) for those on active surveillance (AS). Model combining clinical stage, PSAD, and lesion size improved accuracy for all men and AS (AUC 0.82 [0.736-0.910], p<0.001 and 0.785 [0.666-0.904], p<0.001). In men with prior negative biopsy and persistent suspicion, PSAD (0.90 [0.767-1.000]) was not different from the model (p>0.05), with optimal cutpoint of ≥0.215 ng/mL/cc achieving sensitivity/specificity of 85.7/84.4%., Conclusions: PI-RADSv2 category 3 lesions are often not CSPCa. PSAD predicted CSPCa in men with a prior negative biopsy; however, PSAD alone had limited value, and accuracy improved when using a model incorporating PSAD with clinical stage and MRI lesion size.

BACKGROUND. In PI-RADS version 2.1 (v2.1), atypical transition zone (TZ) nodules (homogeneous circumscribed nodules without full encapsulation) assigned category 2 can be upgraded to category 3 when showing markedly restricted diffusion. The prevalence of prostate cancer (PCa) in DWI-upgraded atypical nodules is unknown. OBJECTIVE. The purpose of this study was to evaluate the prevalence of PCa in DWI-upgraded TZ atypical nodules and compare PCa diagnosis rate with that for conventional score 3 TZ nodules. METHODS. We retrospectively identified 104 consecutive cases of men who underwent MRI-directed transrectal ultrasound-guided targeted biopsy of 109 TZ category 3 or lower nodules performed between January 2015 and July 2018. Three radiologists who were blinded to the scores independently rescored lesions using PI-RADS v2.1. Agreement was assessed by Cohen kappa score. Consensus diagnosis was established by a second-round joint review. The number of TZ atypical nodules with or without DWI-upgraded and conventional score 3 TZ nodules were recorded and compared with targeted biopsy results including any PCa or clinically significant PCa (csPCa, defined as International Society of Urological Pathology [ISUP] grade group ≥ 2) using chi-square analysis. RESULTS. There were 95 PI-RADS v2.1 category 3 (55 conventional T2-weighted MRI score 3 and 40 DWI-upgraded atypical nodules) and 14 category 2 or 1 nodules at consensus review with patient mean age of 64.8 ± 8.4 (SD) years, PSA of 10.6 ± 7.2 ng/mL, and nodule size of 15.1 ± 5.5 mm. Interobserver agreement ranged from slight to substantial for radiologists 1 and 2 (κ = 0.329), radiologists 1 and 3 (κ = 0.548), and radiologists 2 and 3 (κ = 0.652). From the 40 upgraded atypical nodules, 27.5% (11/40) had PCa and 7.5% (3/40) had csPCa (8 ISUP grade 1, 2 ISUP grade 2, 1 ISUP grade 3), compared with 43.6% (24/55) PCa and 20.0% (11/55) csPCa (13 ISUP grade 1, 6 ISUP grade 2, 3 ISUP grade 3, 2 ISUP grade 4) diagnosed in conventional T2-weighted score 3 nodules ( p = .09 for csPCA and p = .11 for PCa). PCa was not diagnosed in any atypical nodule that was not upgraded on DWI. CONCLUSION. The prevalence of PCa in DWI-upgraded TZ atypical nodules was low (≈ 28% for any PCa and ≈ 8% for csPCa) and compared favorably to csPCa diagnosis rates in conventional TZ score 3 nodules. CLINICAL IMPACT. This study validates the DWI upgrade rule introduced in PI-RADS v2.1 for atypical nodules, which showed significant prostate cancer detection rates at targeted biopsy similar to those of conventional T2-weighted MRI TZ score 3 nodules.

Purpose: To evaluate T2-weighted MRI features to differentiate adrenal metastases from lipid-poor adenomas., Materials and Methods: With institutional review board approval, this study retrospectively compared 40 consecutive patients (mean age, 66 years ± 10 [standard deviation]) with metastases to 23 patients (mean age, 60 years ± 15) with lipid-poor adenomas at 1.5- and 3-T MRI between June 2016 and March 2019. A blinded radiologist measured T2-weighted signal intensity (SI) ratio (SI nodule /SI psoas muscle ), T2-weighted histogram features, and chemical shift SI index. Two blinded radiologists (radiologist 1 and radiologist 2) assessed T2-weighted SI and T2-weighted heterogeneity using five-point Likert scales., Results: Subjectively, T2-weighted SI ( P < .001 for radiologist 1 and radiologist 2) and T2-weighted heterogeneity ( P < .001, for radiologist 1 and radiologist 2) were higher in metastases compared with adenomas when assessed by both radiologists. Agreement between the radiologists was substantial for T2-weighted SI (Cohen κ = 0.67) and T2-weighted heterogeneity (κ = 0.62). Metastases had higher T2-weighted SI ratio than adenomas (3.6 ± 1.7 [95% confidence interval {CI}: 0.2, 8.2] vs 2.2 ± 1.0 [95% CI: 0.6, 4.3], P < .001) and higher T2-weighted entropy (6.6 ± 0.6 [95% CI: 4.9, 7.5] vs 5.0 ± 0.8 [95% CI: 3.5, 6.6], P < .001). At multivariate analysis, T2-weighted entropy was the best differentiating feature ( P < . 001). Chemical shift SI index did not differ between metastases and adenomas ( P = .748). Area under the receiver operating characteristic curve (AUC) for T2-weighted SI ratio and T2-weighted entropy were 0.76 (95% CI: 0.64, 0.88) and 0.94 (95% CI: 0.88, 0.99). The logistic regression model combining T2-weighted SI ratio with T2-weighted entropy yielded AUC of 0.95 (95% CI: 0.91, 0.99) and did not differ compared with T2-weighted entropy alone ( P = .268). There was no difference in logistic regression model accuracy comparing the data by either field strength, 1.5- or 3-T MRI ( P > .05)., Conclusion: Logistic regression models combining T2-weighted SI and T2-weighted heterogeneity can differentiate metastases from lipid-poor adenomas. Validation of these preliminary results is required. Keywords: Adrenal, MR-Imaging, Urinary Supplemental material is available for this article. © RSNA, 2020., Competing Interests: Disclosures of Conflicts of Interest: W.T. trainee editorial board member of Radiology: Imaging Cancer. J.A.G. disclosed no relevant relationships. A.U. disclosed no relevant relationships. A.A. disclosed no relevant relationships. N.S. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author is employed by The Ottawa Hospital. Other relationships: disclosed no relevant relationships., (2020 by the Radiological Society of North America, Inc.)

Background: The METastasis Reporting and Data System for Prostate Cancer (MET-RADS-P) guidelines are designed to enable reproducible assessment in detecting and quantifying metastatic disease response using whole-body magnetic resonance imaging (WB-MRI) in patients with advanced prostate cancer (APC). The purpose of our study was to evaluate the inter-observer agreement of WB-MRI examination reports produced by readers of different expertise when using the MET-RADS-P guidelines., Methods: Fifty consecutive paired WB-MRI examinations, performed from December 2016 to February 2018 on 31 patients, were retrospectively examined to compare reports by a Senior Radiologist (9 years of experience in WB-MRI) and Resident Radiologist (after a 6-months training) using MET-RADS-P guidelines, for detection and for primary/dominant and secondary response assessment categories (RAC) scores assigned to metastatic disease in 14 body regions. Inter-observer agreement regarding RAC score was evaluated for each region by using weighted-Cohen's Kappa statistics (K)., Results: The number of metastatic regions reported by the Senior Radiologist (249) and Resident Radiologist (251) was comparable. For the primary/dominant RAC pattern, the agreement between readers was excellent for the metastatic findings in cervical, dorsal, and lumbosacral spine, pelvis, limbs, lungs and other sites (K:0.81-1.0), substantial for thorax, retroperitoneal nodes, other nodes and liver (K:0.61-0.80), moderate for pelvic nodes (K:0.56), fair for primary soft tissue and not assessable for skull due to the absence of findings. For the secondary RAC pattern, agreement between readers was excellent for the metastatic findings in cervical spine (K:0.93) and retroperitoneal nodes (K:0.89), substantial for those in dorsal spine, pelvis, thorax, limbs and pelvic nodes (K:0.61-0.80), and moderate for lumbosacral spine (K:0.44)., Conclusions: We found inter-observer agreement between two readers of different expertise levels to be excellent in bone, but mixed in other body regions. Considering the importance of bone metastases in patients with APC, our results favor the use of MET-RADS-P in response to the growing clinical need for monitoring of metastasis in these patients.

Objective: To compare observation size and apparent diffusion coefficient (ADC) values in Prostate Imaging Reporting and Data System (PI-RADS) v2.1 category 4 and 5 observations to adverse pathological features., Materials and Methods: With institutional review board approval, 267 consecutive men with 3-T MRI before radical prostatectomy (RP) between 2012 and 2018 were evaluated by two blinded radiologists who assigned PI-RADS v2.1 scores. Discrepancies were resolved by consensus. A third blinded radiologist measured observation size and ADC (ADC.mean, ADC.min [lowest ADC within an observation], ADC.ratio [ADC.mean/ADC.peripheral zone {PZ}]). Size and ADC were compared to pathological stage and Gleason score (GS) using t tests, ANOVA, Pearson correlation, and receiver operating characteristic (ROC) analysis., Results: Consensus review identified 267 true positive category 4 and 5 observations representing 83.1% (222/267) PZ and 16.9% (45/267) transition zone (TZ) tumors. Inter-observer agreement for PI-RADS v2.1 scoring was moderate (K = 0.45). Size was associated with extra-prostatic extension (EPE) (19 ± 8 versus 14 ± 6 mm, p < 0.001) and seminal vesicle invasion (SVI) (24 ± 9 versus 16 ± 7 mm, p < 0.001). Size ≥ 15 mm optimized the accuracy for EPE with area under the ROC curve (AUC) and sensitivity/specificity of 0.68 (CI 0.62-0.75) and 63.2%/65.6%. Size ≥ 19 mm optimized the accuracy for SVI with AUC/sensitivity/specificity of 0.75 (CI 0.66-0.83)/69.4%/70.6%. ADC metrics were not associated with pathological stage. Larger observation size (p = 0.032), lower ADC.min (p = 0.010), and lower ADC.ratio (p = 0.010) were associated with higher GS. Size correlated better to higher Gleason scores (p = 0.002) compared to ADC metrics (p = 0.09-0.11)., Conclusion: Among PI-RADS v2.1 category 4 and 5 observations, size was associated with higher pathological stage whereas ADC metrics were not. Size, ADC.minimum, and ADC.ratio differed in tumors stratified by Gleason score., Key Points: • Among PI-RADS category 4 and 5 observations, size but not ADC can differentiate between tumors by pathological stage. • An observation size threshold of 15 mm and 19 mm optimized the accuracy for diagnosis of extra-prostatic extension and seminal vesicle invasion. • Among PI-RADS category 4 and 5 observations, size, ADC.minimum, and ADC.ratio differed comparing tumors by Gleason score.

OBJECTIVE. The objective of our study was to subjectively and quantitatively assess shape features of peripheral zone (PZ) tumors at DWI compared with pathologic outcomes. MATERIALS AND METHODS. During the study period, 241 consecutive men with PZ dominant prostate tumors underwent 3-T MRI including DWI before undergoing radical prostatectomy. DW images of these patients were retrospectively assessed by two blinded radiologists. The reviewers assigned Prostate Imaging Reporting and Data System (PI-RADS) shape categories (round or oval, crescentic [i.e., conforming to PZ], linear or wedge-shaped) and segmented tumors for quantitative shape analysis. Discrepancies were resolved by consensus. Comparisons were performed with Gleason score (GS) and pathologic stage. RESULTS. Consensus review results were as follows: 63.9% (154/241) of tumors were round or oval; 22.8% (55/241), crescentic; and 13.3% (32/241), linear or wedge-shaped. Agreement for shape assessment was moderate (κ = 0.41). Round or oval tumors were higher grade (GS 6 = 1.3%, GS 7 = 78.0%, GS ≥ 8 = 20.7%) than crescentic tumors (GS 6 = 9.1%, GS 7 = 74.6%, GS ≥ 8 = 16.3%) and linear or wedge-shaped tumors (GS 6 = 6.3%, GS 7 = 78.1%, GS ≥ 8 = 15.6%) ( p = 0.011). In addition, round or oval tumors had higher rates of extraprostatic extension (EPE) and seminal vesicle invasion (SVI) (EPE and SVI: 70.1% and 26.0%) than crescentic tumors (67.3% and 9.1%; p = 0.003) and linear or wedge-shaped tumors (40.6% and 9.4%; p = 0.008). Quantitatively, the shape features termed "circularity" and "roundness" were associated with EPE ( p < 0.001 and p = 0.003), SVI ( p < 0.001 and p = 0.029), and increasing GS ( p = 0.009 and p = 0.021), but there was overlap between groups. CONCLUSION. In this study, approximately 10% of resected PZ tumors were linear or wedge-shaped on DWI. PZ tumors that were judged subjectively and evaluated quantitatively to be round or oval were associated with increased prostate cancer aggressiveness.

Magnetic resonance imaging of the upper tract (pyelocalyces and ureters) or MR Urography (MRU) is technically possible and when performed correctly offers similar visualization of the upper tracts and for detection of non-calculous diseases of the collecting system similar specificity but with lower sensitivity compared to CTU. MRU provides the ability to simultaneously image the kidneys and urinary bladder with improved soft tissue resolution, better tissue characterization and when combined with assessment of the upper tract, a comprehensive examination of the urinary system. MRU requires meticulous attention to technical details and is a longer more demanding examination compared to CTU. Advances in MR imaging techniques including: parallel imaging, free-breathing motion compensation techniques and compressed sensing can dramatically shorten examination times and improve image quality and patient tolerance for the exam. This review article discusses updates in the MRU technique, summarizes clinical indications and opportunities for MRU in clinical practice and reviews advantages and disadvantages of MRU compared to CTU.

Purpose: To review the computed tomography and magnetic resonance imaging manifestations of acute and chronic renal infections that may mimic malignancy and to provide useful tips to establish an imaging diagnosis., Conclusion: Acute and chronic bacterial pyelonephritis are usually readily diagnosed clinically and on imaging when the diagnosis is suspected based upon clinical presentation. When unsuspected, focal, extensive or mass-like, acute and chronic bacterial pyelonephritis may mimic infiltrative tumours such as urothelial cell carcinoma (UCC), lymphoma, and metastatic disease. Infection may be suspected when patients are young and otherwise healthy when there is marked associated perinephric changes and in the absence of metastatic adenopathy or disease elsewhere in the abdomen and pelvis. Renal abscesses, from bacterial or atypical microbial agents, can appear as complex cystic renal masses mimicking cystic renal cell carcinoma. Associated inflammatory changes in and around the kidney and local invasion favour infection. Emphysematous pyelonephritis can mimic necrotic or fistulizing tumour; however, infection is more likely and should always be considered first. Xanthogranulomatous pyelonephritis can mimic malignancy when focal or multifocal and in cases without associated renal calculi. Malacoplakia is an inflammatory process that may mimic malignancy and should be considered in patients with chronic infection. Bacillus Calmette-Guerin (BCG)-induced pyelonephritis is rare but can mimic renal malignancy and should be considered in patients presenting with a renal mass when being treated with BCG for urinary bladder UCC., (Copyright © 2019 Canadian Association of Radiologists. Published by Elsevier Inc. All rights reserved.)

OBJECTIVE. The purpose of this study is to evaluate dynamic contrast-enhanced (DCE) MRI (DCE-MRI)-upgraded Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) peripheral zone (PZ) observations stratified by a size threshold of 15 mm. MATERIALS AND METHODS. Two blinded radiologists independently assessed 301 patients with 326 clinically significant tumors (Gleason score [GS] ≥ 7) using multiparametric MRI performed before radical prostatectomy (RP) between 2012 and 2017 and then assigned PI-RADSv2 scores for the tumors. PI-RADSv2 category 3 PZ observations upgraded on the basis of abnormal DCE-MRI findings were tabulated, agreement was calculated, and discrepancies were resolved by consensus. The rate of detection of clinically significant cancer among upgraded observations was calculated. Size was measured at consensus review and was compared with pathologic outcomes on the basis of the PI-RADSv2 size threshold of 15 mm or more, with the use of chi-square tests. RESULTS. Reader 1 identified 5.2% (17/326) of DCE-MRI-upgraded PZ observations, and reader 2 identified 8.3% (27/326) of such observations. Interobserver agreement for PI-RADSv2 scoring was moderate (κ = 0.42) overall, but it was fair (κ = 0.23) when only DCE-MRI-upgraded observations were considered. Of the upgraded observations, which had a mean (± SD) size of 14 ± 6 mm (range, 6-29 mm), 10.4% (34/326) were agreed on after consensus review. Size smaller than 15 mm was noted for 61.8% (21/34) of observations. Among DCE-MRI-upgraded PZ observations, true- and false-positive detection rates for significant cancer were 91.2% (31/34) and 8.8% (3/34), respectively. Observations 15 mm or larger had no false-positive diagnoses and higher rates of extraprostatic extension (84.6% [11/13] vs 38.1% [8/21]; p = 0.016); however, there was no difference in GS ( p = 0.354) compared with observations less than 15 mm in size. CONCLUSION. PZ observations upgraded on the basis of abnormal DCE-MRI findings have a high likelihood of being clinically significant cancer; however, agreement between readers was low. DCE-MRI-upgraded tumors of 15 mm or larger had no false-positive diagnoses and higher rates of extraprostatic extension, suggesting that they could be assigned to PI-RADSv2 assessment category 5.

OBJECTIVE. The purpose of this study was to assess prostate multiparametric MRI (mpMRI) before and after intervention by a director of prostate imaging. MATERIALS AND METHODS. Images from prostate mpMRI examinations at four peripheral institutions (five 1.5-T systems) were studied. DICOM headers were analyzed for T2-weighted, DWI, and dynamic contrast-enhanced technical specifications. Reports were retrieved, and a blinded radiologist compared them with those from the regional academic referral center (3-T system) and Prostate Imaging and Data Reporting System version 2 (PI-RADSv2) technical specifications. Data were reevaluated after intervention by a director of prostate imaging. Comparisons were performed by chi-square analysis. RESULTS. Except for having insufficient DWI spatial resolution, the referral center fully complied with PI-RADSv2. For peripheral systems, compliance with PI-RADSv2 technical specifications improved from baseline to after intervention. For T2-weighted imaging, compliance with spatial resolution increased from 40% (two of five MRI systems) to 100% (all five systems) ( p = 0.038). For DWI, spatial resolution compliance increased from 20% to 100%. For modified DWI, spatial resolution compliance to improve image quality at 1.5 T (matrix, 100 × 100; FOV, 28 × 28 cm; slice thickness, 4 mm) increased from 60% (b value ≥ 1400 s/mm 2 ) to 100% ( p = 0.114). For dynamic contrast-enhanced imaging, spatial resolution compliance increased from 60% to 100% ( p = 0.114), temporal resolution compliance increased from 20% (≤ 10 seconds) to 100% ( p = 0.10), and acquisition time compliance increased from 60% (≥ 2 minutes) to 100% ( p = 0.114). Only one of the four peripheral centers provided PI-RADSv2 scores, but all of them did after the intervention ( p = 0.028). CONCLUSION. A director of prostate imaging may drive standardization of prostate MRI performance and reporting within specified geographic regions.

Objective: To assess whether MRI can differentiate low-grade from high-grade T1a cc-RCC., Materials and Methods: With IRB approval, 49 consecutive solid < 4 cm cc-RCC (low grade [Grade 1 or 2] N = 38, high grade [Grade 3] N = 11) with pre-operative MRI before nephrectomy were identified between 2013 and 2018. Tumor size, apparent diffusion coefficient (ADC) histogram analysis, enhancement wash-in and wash-out rates, and chemical shift signal intensity index (SI index) were assessed by a blinded radiologist. Subjectively, two blinded Radiologists also assessed for (1) microscopic fat, (2) homogeneity (5-point Likert scale), and (3) ADC signal (relative to renal cortex); discrepancies were resolved by consensus. Outcomes were studied using Chi square, multivariate analysis, logistic regression modeling, and ROC. Inter-observer agreement was assessed using Cohen's kappa., Results: Tumor size was 24 ± 7 (13-39) mm with no association to grade (p = 0.45). Among quantitative features studied, corticomedullary phase wash-in index (p = 0.015), SI index (p = 0.137), and tenth-centile ADC (p = 0.049) were higher in low-grade tumors. 36.8% (14/38) low-grade tumors versus zero high-grade tumors demonstrated microscopic fat (p = 0.015; Kappa = 0.67). Microscopic fat was specific for low-grade disease (100.0% [71.5-100.0]) with low sensitivity (36.8% [21.8-54.6]). Other subjective features did not differ between groups (p > 0.05). A logistic regression model combining microscopic fat + wash-in index + tenth-centile-ADC yielded area under ROC curve 0.98 (Confidence Intervals 0.94-1.0) with sensitivity/specificity 87.5%/100%., Conclusion: The combination of microscopic fat, higher corticomedullary phase wash-in and higher tenth-centile ADC is highly accurate for diagnosis of low-grade disease among T1a clear cell RCC.