BACKGROUND. Bosniak classification version 2019 (v2019) was a major revision to version 2005 (v2005) that defined cystic renal mass subclasses on the basis of wall or septa features. OBJECTIVE. The purpose of the study was to determine the proportion of malignancy within cystic renal masses stratified by Bosniak classification v2019 class and feature-based subclass. EVIDENCE ACQUISITION. MEDLINE and Embase databases were searched on July 24, 2023, for studies published in 2019 or later that reported cystic renal masses that underwent renal-mass CT or MRI, were assessed using Bosniak classification v2019, and had a reference standard (histopathology indicating benignancy or malignancy or ≥ 5 years of imaging follow-up indicating benignancy). Study authors were contacted to provide subclass-stratified data. Pooled proportions of malignancy stratified by v2019 class and subclass were determined using meta-analysis. EVIDENCE SYNTHESIS. The analysis included 12 studies reporting 966 patients with 975 cystic masses. No class I mass was malignant. Pooled proportions of malignancy by class were as follows: II, 9% (95% CI: 5-17%); IIF, 26% (95% CI: 13-46%); III, 80% (95% CI: 71-87%); and IV, 88% (95% CI: 83-91%). Pooled proportions of malignancy by subclass were as follows: IIF with many smooth, thin septa, 10% (95% CI: 2-33%); IIF with minimal wall or septal thickening, 47% (95% CI: 18-77%); IIF with heterogeneous T1 hyperintensity, 26% (95% CI: 8-57%); III with a thick, smooth wall or septa, 78% (95% CI: 60-90%); III with obtuse protrusion(s) 3 mm or less, 84% (95% CI: 77-90%); IV with acute protrusion(s) of any size, 88% (95% CI: 80-93%); and IV with obtuse protrusion(s) 4 mm or greater, 86% (95% CI: 77-91%). The proportion of malignancy was 41% for IIF masses with histopathology reference versus 2% for IIF masses with imaging follow-up reference. In four studies performing intraindividual comparisons of v2005 versus v2019, the proportions of malignancy were as follows: class IIF, 24% versus 42% (p = .13); III, 74% versus 77% (p = .72); and IV, 79% versus 84% (p = .22). CONCLUSION. Bosniak IIF masses had higher malignancy rates when histopathology rather than imaging follow-up was the reference standard, indicating verification bias. All Bosniak III and IV subclasses had high malignancy rates. CLINICAL IMPACT. The results improve understanding of imaging-based cystic renal-mass classification and may inform development of future renal-mass classification systems. TRIAL REGISTRATION. PROSPERO (International Prospective Register of Systematic Reviews) CRD42023472140.

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.

The establishment of the Liver Imaging Reporting and Data System (LI-RADS) in 2011 provided a comprehensive approach to standardized imaging, interpretation, and reporting of liver observations in patients diagnosed with or at risk for hepatocellular carcinoma (HCC). Each set of algorithms provides criteria pertinent to the various components of HCC management including surveillance, diagnosis, staging, and treatment response supported by a detailed lexicon of terms applicable to a wide range of liver imaging scenarios. Before its widespread adoption, the variability in the terminology of diagnostic criteria and definitions of imaging features led to significant challenges in patient management and made it difficult to replicate findings or apply them consistently. The integration of LI-RADS into the clinical setting has enhanced the efficiency and clarity of communication between radiologists, referring providers, and patients by employing a uniform language that averts miscommunications. LI-RADS has been strengthened with its integration into the American Association for Study of Liver Diseases practice guidelines. We will provide the background on the initial development of LI-RADS and reasons for development to serve as a starting point for conveying the system’s benefits and evolution over the years. We will also suggest strategies for the implementation and maintenance of a LI-RADS program will be discussed.

HomeRadioGraphicsVol. 43, No. 3 PreviousNext UltrasoundRadioGraphics FundamentalsO-RADS US Risk Stratification and Management System: Case-based Learning Approach for Daily PracticeKalesha Hack, Lori Strachowski, Rochelle F. Andreotti, Hournaz Ghandehari, Priyanka Jha, Christopher Lim, Chirag Patel, Phyllis Glanc Kalesha Hack, Lori Strachowski, Rochelle F. Andreotti, Hournaz Ghandehari, Priyanka Jha, Christopher Lim, Chirag Patel, Phyllis Glanc Author AffiliationsFrom the Department of Medical Imaging, University of Toronto, Sunnybrook Health Sciences Centre, MG160, 2075 Bayview Ave, Toronto, ON, Canada M4N 3M5 (K.H., H.G., C.L., C.P., P.G.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (L.S., P.J.); and Department of Radiology, Vanderbilt University, Nashville, Tenn (R.F.A.).Address correspondence to P.G. (email: [email protected]).Kalesha HackLori StrachowskiRochelle F. AndreottiHournaz GhandehariPriyanka JhaChristopher LimChirag PatelPhyllis Glanc Published Online:Feb 23 2023https://doi.org/10.1148/rg.220079MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In AbstractUnderstanding the O-RADS US lexicon and how to apply it accurately can improve care in daily practice by decreasing unnecessary follow-up and surgery and promoting rapid referral to a gynecologic oncologist.Suggested ReadingsAndreotti RF, Timmerman D, Benacerraf BR, et al. Ovarian-Adnexal Reporting Lexicon for Ultrasound: A White Paper of the ACR Ovarian-Adnexal Reporting and Data System Committee. J Am Coll Radiol 2018;15(10):1415–1429 [Published correction appears in J Am Coll Radiol 2019;16(3):403–406.]. Crossref, Medline, Google ScholarAndreotti RF, Timmerman D, Strachowski LM, et al. O-RADS US risk stratification and management system: a consensus guideline from the ACR Ovarian-Adnexal Reporting and Data System Committee. Radiology 2020;294(1):168–185. Link, Google ScholarBasha MAA, Metwally MI, Gamil SA, et al. Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses. Eur Radiol 2021;31(2):674–684. Crossref, Medline, Google ScholarCao L, Wei M, Liu Y, et al. Validation of American College of Radiology Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US): Analysis on 1054 adnexal masses. Gynecol Oncol 2021;162(1):107–112. Crossref, Medline, Google ScholarGiede KC, Kieser K, Dodge J, Rosen B. Who should operate on patients with ovarian cancer? An evidence-based review. Gynecol Oncol 2005;99(2):447–461. Crossref, Medline, Google ScholarGlanc P, Benacerraf B, Bourne T, et al. First international consensus report on adnexal masses: management recommendations. J Ultrasound Med 2017;36(5):849–863. Crossref, Medline, Google ScholarHack K, Gandhi N, Kahn D, Glanc P. OC03.05: External validation O-RADS ultrasound risk stratification and management system. Ultrasound Obstet Gynecol 2021;58(S1):8–9. Crossref, Google ScholarHack K, Glanc P. The Abnormal Ovary: Evolving Concepts in Diagnosis and Management. Obstet Gynecol Clin North Am 2019;46(4):607–624. Crossref, Medline, Google ScholarLevine D, Patel MD, Suh-Burgmann EJ, et al. Simple adnexal cysts: SRU consensus conference update on follow-up and reporting. Radiology 2019;293(2):359–371. Link, Google ScholarPi Y, Wilson MP, Katlariwala P, et al. Diagnostic accuracy and inter-observer reliability of the O-RADS scoring system among staff radiologists in a North American academic clinical setting. Abdom Radiol (NY) 2021;46(10):4967–4973. Crossref, Medline, Google ScholarSadowski, EA, et al. O-RADS MRI Risk Stratification System: Guide for Assessing Adnexal Lesions from the ACR O-RADS Committee. Radiology 2022;303:35–47. Link, Google ScholarStrachowski LM, Jha P, Chawla TP, et al. O-RADS for Ultrasound: A User's Guide, From the AJR Special Series on Radiology Reporting and Data Systems. AJR Am J Roentgenol 2021;216(5):1150–1165. Crossref, Medline, Google ScholarTimmerman D. Lack of standardization in gynecological ultrasonography. Ultrasound Obstet Gynecol 2000;16(5):395–398. Crossref, Medline, Google ScholarArticle HistoryReceived: Apr 8 2022Revision requested: May 16 2022Revision received: June 3 2022Accepted: June 8 2022Published online: Feb 23 2023 FiguresReferencesRelatedDetailsAccompanying This ArticleO-RADS US Risk Stratification and Management System: Case-based Learning Approach for Daily PracticeFeb 23 2023Default Digital Object SeriesRecommended Articles O-RADS US Risk Stratification and Management System: A Consensus Guideline from the ACR Ovarian-Adnexal Reporting and Data System CommitteeRadiology2019Volume: 294Issue: 1pp. 168-185Physiologic Ovarian Cysts versus Other Ovarian and Adnexal Pathologic Changes in the Preadolescent and Adolescent Population: US and Surgical Follow-upRadiology2019Volume: 292Issue: 1pp. 172-178Ovarian Cancer Detection in Average-Risk Women: Classic- versus Nonclassic-appearing Adnexal Lesions at USRadiology2022Volume: 303Issue: 3pp. 603-610Benign-appearing Incidental Adnexal Cysts at US, CT, and MRI: Putting the ACR, O-RADS, and SRU Guidelines All TogetherRadioGraphics2022Volume: 42Issue: 2pp. 609-624MRI of Borderline Epithelial Ovarian Tumors: Pathologic Correlation and Diagnostic ChallengesRadioGraphics2022Volume: 42Issue: 7pp. 2095-2111See More RSNA Education Exhibits O-RADS: Case Based LearningDigital Posters2021Update on Ovarian Neoplasm: What Radiologists Need to Know on Clinical, Laboratory, and an Algorithmic Imaging Approach to Benign versus Malignant Neoplasms Digital Posters2019Multifaceted Pancreatic Serous Cystadenoma and Its Differential Diagnosis at MRIDigital Posters2019 RSNA Case Collection Endometrioma RSNA Case Collection2022Pancreatic serous cystadenoma RSNA Case Collection2021Intraductal Papillary Mucinous Neoplasm RSNA Case Collection2020 Vol. 43, No. 3 Slide PresentationAbbreviations Abbreviations: O-RADS Ovarian-Adnexal Reporting and Data System Metrics Altmetric Score PDF download

BACKGROUND. Adrenal washout CT is not useful for evaluating incidental adrenal masses in patients without known or suspected primary extraadrenal malignancy. OBJECTIVE. The purpose of our study was to evaluate the diagnostic utility of adrenal mass biopsy in patients without known or suspected extraadrenal primary malignancy. METHODS. This retrospective six-center study included 69 patients (mean age, 56 years; 32 men, 37 women) without known or suspected extraadrenal primary malignancy who underwent image-guided core needle biopsy between January 2004 and June 2021 of a mass suspected to be arising from the adrenal gland. Biopsy results were classified as diagnostic or nondiagnostic. For masses resected after biopsy, histopathologic concordance was assessed between diagnoses from biopsy and resection. Masses were classified as benign or malignant by resection or imaging follow-up, and all nondiagnostic biopsies were classified as false results. RESULTS. The median mass size was 7.4 cm (range, 1.9-19.2 cm). Adrenal mass biopsy had a diagnostic yield of 64% (44/69; 95% CI, 51-75%). After biopsy, 25 masses were resected, and 44 had imaging follow-up. Of the masses that were resected after diagnostic biopsy, diagnosis was concordant between biopsy and resection in 100% (12/12). Of the 13 masses that were resected after nondiagnostic biopsy, the diagnosis from resection was benign in eight masses and malignant in five masses. The 44 masses with imaging follow-up included one mass with diagnostic biopsy yielding benign adenoma and two masses with nondiagnostic biopsy results that were classified as malignant by imaging follow-up. Biopsy had overall sensitivity and specificity for malignancy of 73% (22/30) and 54% (21/39), respectively; diagnostic biopsies had sensitivity and specificity for malignancy of 96% (22/23) and 100% (21/21), respectively. Among nine nondiagnostic biopsies reported as adrenocortical neoplasm, six were classified as malignant by the reference standard (resection showing adrenocortical carcinoma in four, resection showing adrenocortical neoplasm of uncertain malignant potential in one, imaging follow-up consistent with malignancy in one). CONCLUSION. Adrenal mass biopsy had low diagnostic yield, with low sensitivity and low specificity for malignancy. A biopsy result of adrenocortical neoplasm did not reliably differentiate benign and malignant adrenal masses. CLINICAL IMPACT. Biopsy appears to have limited utility for the evaluation of incidental adrenal masses in patients without primary extraadrenal malignancy.

Renal cell carcinoma (RCC) is usually diagnosed in older adults (the median age of diagnosis is 64 years). Although less common in patients younger than 45 years, RCCs in young adults differ in clinical manifestation, pathologic diagnosis, and prognosis. RCCs in young adults are typically smaller, are more organ confined, and manifest at lower stages of disease. The proportion of clear cell RCC is lower in young adults, while the prevalence of familial renal neoplastic syndromes is much higher, and genetic testing is routinely recommended. In such syndromic manifestations, benign-appearing renal cysts can harbor malignancy. Radiologists need to be familiar with the differences of RCCs in young adults and apply an altered approach to diagnosis, treatment, and surveillance. For sporadic renal neoplasms, biopsy and active surveillance are less often used in young adults than in older adults. RCCs in young adults are overall associated with better disease-specific survival after surgical treatment, and minimally invasive nephron-sparing treatment options are preferred. However, surveillance schedules, need for biopsy, decision for an initial period of active surveillance, type of surgery (enucleation or wide-margin partial nephrectomy), and utilization of ablative therapy depend on the presence and type of underlying familial renal neoplastic syndrome. In this pictorial review, syndromic, nonsyndromic, and newer RCC entities that are common in young adults are presented. Their associated unique epidemiology, characteristic imaging and pathologic traits, and key aspects of surveillance and management of renal neoplasms in young adults are discussed. The vital role of the informed radiologist in the multidisciplinary management of RCCs in young adults is highlighted. Online supplemental material is available for this article. ©RSNA, 2022.

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’]). 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. 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. 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. • 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.

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. 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. 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. 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.

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

Ablative (percutaneous and stereotactic) thermal and radiotherapy procedures for management of both primary and metastatic renal cell carcinoma are increasing in popularity in clinical practice. Data suggest comparable efficacy with lower cost and morbidity compared to nephrectomy. Ablative therapies may be used alone or in conjunction with surgery or chemotherapy for treatment of primary tumor and metastatic disease. Imaging plays a crucial role in pre-treatment selection and planning of ablation, intra-procedural guidance, evaluation for complications, short- and long-term post-procedural surveillance of disease, and treatment response. Treatment response and disease recurrence may differ considerably after ablation, particularly for stereotactic radiotherapy, when compared to conventional surgical and chemotherapies. This article reviews the current and emerging role of imaging for ablative therapy of renal cell carcinoma.

To determine if pharmacokinetic modeling of DCE-MRI can diagnose CS-PCa in PI-RADS category 3 PZ lesions with subjective negative DCE-MRI. 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. 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). 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.

OBJECTIVE. The purpose of this study was to determine whether quantitative T2-weighted imaging and apparent diffusion coefficient (ADC) texture features of bladder cancer and extravesical fat are predictive of muscle invasive bladder cancer (category ≥ T2) and extravesical (category ≥ T3) disease after transurethral resection of a bladder tumor (TURBT). MATERIALS AND METHODS. In this retrospective study, 36 patients (27 men, nine women; mean age, 71 years) were identified who underwent post-TURBT MRI followed by cystectomy without intervening treatment from August 2011 through October 2016. Texture features of bladder cancer and extravesical fat adjacent to the tumor on T2-weighted and ADC images were extracted and compared between category ≤ T2 versus ≥ T3 and category T1 versus ≥ T2 tumors by means of Kruskal-Wallis or Mann-Whitney U test. Multivariate logistic regression analysis was performed, and ROC curves were calculated. RESULTS. Twenty-six of the 36 (72%) tumors were ≥ T2, and 53% (19/36) were ≥ T3. In multivariate analysis, bladder cancer entropy on T2-weighted images (p = 0.006; odds ratio [OR], 4.56; 95% CI, 1.49-20.41; AUC, 0.85) and ADC maps (p = 0.019; OR, 2.24; 95% CI, 1.13-5.31; AUC, 0.80) and extravesical fat entropy on T2-weighted images (p = 0.005; OR, 17.50; 95% CI, 3.01-200.80; AUC, 0.84) and ADC maps (p = 0.002; OR, 6.54; 95% CI, 1.90-32.40; AUC, 0.82) remained greater for ≥ T3 than for ≤ T2 tumors. In multivariate analysis, bladder cancer entropy on ADC maps (p = 0.027; OR, 2.11; 95% CI, 1.08-5.03; AUC, 0.76) and extravesical fat entropy on T2-weighted images (p = 0.010; OR, 5.33; 95% CI, 1.25-3.79; AUC, 0.78) and ADC maps (p = 0.029; OR, 3.80; 95% CI, 1.25-16.97; AUC, 0.74) remained greater for category ≥ T2 compared with category T1 tumors. CONCLUSION. Greater entropy of primary bladder cancers and extravesicular fat was observed in category ≥ T3 than in category ≤ T2 and in category ≥ T2 than in category T1 tumors. MRI texture analysis can help with local bladder cancer staging in patients who have undergone TURBT and may serve as a biomarker for higher local category bladder cancers.

The Liver Imaging Reporting and Data System (LI-RADS®) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver observations in individuals at high risk for hepatocellular carcinoma (HCC). LI-RADS is supported and endorsed by the American College of Radiology (ACR). Upon its initial release in 2011, LI-RADS applied only to liver observations identified at CT or MRI. It has since been refined and expanded over multiple updates to now also address ultrasound-based surveillance, contrast-enhanced ultrasound for HCC diagnosis, and CT/MRI for assessing treatment response after locoregional therapy. The LI-RADS 2018 version was integrated into the HCC diagnosis, staging, and management practice guidance of the American Association for the Study of Liver Diseases (AASLD). This article reviews the major LI-RADS updates since its 2011 inception and provides an overview of the currently published LI-RADS algorithms.

OBJECTIVE. The objective of this article is to review the burgeoning role of percutaneous renal mass biopsy (RMB). CONCLUSION. Percutaneous RMB is safe, accurate, and indicated for an expanded list of clinical scenarios. The chief scenarios among them are to prevent treatment of benign masses and help select patients for active surveillance (AS). Imaging characterization of renal masses has improved; however, management decisions often depend on a histologic diagnosis and an assessment of biologic behavior of renal cancers, both of which are currently best achieved with RMB.

Suboptimal reporting in the publication of imaging research studies is a growing concern. Deficient and incomplete reporting prevents the evaluation of the validity, replicability, and the overall quality of the research. Reporting guidelines are checklists designed to guide researchers about the minimum information to be provided in research studies to allow for adequate quality appraisal and to assess generalizability. They are a powerful tool to allow key stakeholders such as journal editors, peer reviewers, funding agencies, and readers to better identify robust health research. The Enhancing the QUAlity and Transparency of Health Research Network is an international initiative that attempts to improve the reporting practices of a variety of health research study designs by providing the resources required to develop, disseminate, and implement reporting guidelines. In this review, we elaborate on the impact of good reporting on imaging research, and the different types of guidelines relevant for the various study designs applicable in imaging research.

BACKGROUND & AIMS:The Liver Imaging Reporting and Data System (LI-RADS) categorizes observations from imaging analyses of high-risk patients based on the level of suspicion for hepatocellular carcinoma (HCC) and overall malignancy. The categories range from definitely benign (LR-1) to definitely HCC (LR-5), malignancy (LR-M), or tumor in vein (LR-TIV) based on findings from computed tomography or magnetic resonance imaging. However, the actual percentage of HCC and overall malignancy within each LI-RADS category is not known. We performed a systematic review to determine the percentage of observations in each LI-RADS category for computed tomography and magnetic resonance imaging that are HCCs or malignancies. METHODS:We searched the MEDLINE, Embase, Cochrane CENTRAL, and Scopus databases from 2014 through 2018 for studies that reported the percentage of observations in each LI-RADS v2014 and v2017 category that were confirmed as HCCs or other malignancies based on pathology, follow-up imaging analyses, or response to treatment (reference standard). Data were assessed on a per-observation basis. Random-effects models were used to determine the pooled percentages of HCC and overall malignancy for each LI-RADS category. Differences between categories were compared by analysis of variance of logit-transformed percentage of HCC and overall malignancy. Risk of bias and concerns about applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool. RESULTS:Of 454 studies identified, 17 (all retrospective studies) were included in the final analysis, consisting of 2760 patients, 3556 observations, and 2482 HCCs. The pooled percentages of observations confirmed as HCC and overall malignancy, respectively, were 94% (95% confidence interval [CI] 92%-96%) and 97% (95% CI 95%-99%) for LR-5, 74% (95% CI 67%-80%) and 80% (95% CI 75%-85%) for LR-4, 38% (95% CI 31%-45%) and 40% (95% CI 31%-50%) for LR-3, 13% (95% CI 8%-22%) and 14% (95% CI 9%-21%) for LR-2, 79% (95% CI 63%-89%) and 92% (95% CI 77%-98%) for LR-TIV, and 36% (95% CI 26%-48%) and 93% (95% CI 87%-97%) for LR-M. No malignancies were found in the LR-1 group. The percentage of HCCs and overall malignancies confirmed differed significantly among LR groups 2-5 (P < .00001). Patient selection was the most frequent factor that affected bias risk, because of verification bias and case-control study design. CONCLUSIONS:In a systematic review, we found that increasing LI-RADS categories contained increasing percentages of HCCs and overall malignancy based on reference standard confirmation. Of observations categorized as LR-M, 93% were malignancies and 36% were confirmed as HCCs. The percentage of HCCs found in the LR-2 and LR-3 categories indicate the need for a more active management strategy than currently recommended. Prospective studies are needed to validate these findings. PROSPERO number CRD42018087441.

While many institutions perform MRI during the work‐up of urinary bladder cancer, others use MRI rarely if at all, possibly due to a variation in the reported staging accuracy and unfamiliarity with the potential benefits of performing MRI. Through increased application of functional imaging techniques including diffusion‐weighted imaging (DWI) and dynamic contrast‐enhanced (DCE) imaging, there has been a resurgence of interest regarding evaluation of bladder cancer with MRI. Several recent meta‐analyses have shown that MRI is accurate at differentiating between ≤T1 and T2 disease (with pooled sensitivity/specificity of ∼90/80%) and differentiating between T2 and ≥T3 disease. DWI and DCE, in combination with high‐resolution T2‐weighted images, improves detection and possibly local staging accuracy of bladder cancer. High b value echo‐planar DWI is particularly valuable for tumor detection. Zoomed field of view and segmented readout DWI techniques improve image quality by reducing susceptibility artifact, while methods to extract calculated high b value images save time and improve the contrast‐to‐noise ratio. DCE traditionally required imaging of the pelvis with high temporal but lower spatial resolution; however, advances in parallel and keyhole imaging techniques can preserve spatial resolution. The use of compressed sensing reconstruction may improve utilization of DCE of the bladder, especially when imaging the abdomen simultaneously, as in MR urography. Quantitative imaging analysis of bladder cancer using pharmacokinetic modeling of DCE, apparent diffusion coefficient values, and texture analysis may enable radiomic assessment of bladder cancer grade and stage.Level of Evidence: 3Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2018;48:882–896.

PurposeTo assess the ability of magnetic resonance imaging (MRI) to diagnose extraprostatic extension (EPE) in prostate cancer. Materials and MethodsWith Institutional Review Board (IRB) approval, 149 men with 170 0.5mL tumors underwent preoperative 3T MRI followed by radical prostatectomy (RP) between 2012-2015. Two blinded radiologists (R1/R2) assessed tumors using Prostate Imaging Reporting and Data System (PI-RADS) v2, subjectively evaluated for the presence of EPE, measured tumor size, and length of capsular contact (LCC). A third blinded radiologist, using MRI-RP-maps, measured whole-lesion: apparent diffusion coefficient (ADC) mean/centile and histogram features. Comparisons were performed using chi-square, logistic regression, and receiver operator characteristic (ROC) analysis. ResultsThe subjective EPE assessment showed high specificity (SPEC=75.4/91.3% [R1/R2]), low sensitivity (SENS=43.3/43.6% [R1/R2]), and area-under (AU) ROC curve=0.67 (confidence interval [CI] 0.61-0.73) R1 and 0.61 (CI 0.53-0.70) R2; (k=0.33). PI-RADS v2 scores were strongly associated with EPE (P<0.001 / P=0.008; R1/R2) with AU-ROC curve=0.72 (0.64-0.79) R1 and 0.61 (0.53-0.70) R2; (k=0.44). Tumors with EPE were larger (18.87.8 [median 17, range 6-51] vs. 18.84.9 [12, 6-28] mm) and had greater LCC (21.114.9 [16, 1-85] vs. 13.66.1 [11.5, 4-30] mm); P<0.001 and 0.002, respectively. AU-ROC for size was 0.73 (0.64-0.80) and LCC was 0.69 (0.60-0.76), respectively. Optimal SENS/SPEC for diagnosis of EPE were: size 15mm=67.7/66.7% and LCC 11mm=84.9/44.8%. 10(th)-centile ADC and ADC entropy were both associated with EPE (P=0.02 and<0.001), with AU-ROC=0.56 (0.47-0.65) and 0.76 (0.69-0.83), respectively. Optimal SENS/SPEC for diagnosis of EPE with entropy 6.99 was 63.3/75.0%. 25(th)-centile ADC trended towards being significantly lower with EPE (P=0.06) with no difference in other ADC metrics (P=0.25-0.88). Size, LCC, and ADC entropy improved sensitivity but reduced specificity compared with subjective analysis with no difference in overall accuracy (P=0.38). ConclusionMeasurements of tumor size, capsular contact, and ADC entropy improve sensitivity but reduce specificity for diagnosis of EPE compared to subjective assessment. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:176-185.

OBJECTIVEThe objective of our study was to compare Prostate Imaging Reporting and Data System version 1 (PI-RADSv1) and Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) for the detection of peripheral zone (PZ) Gleason score 3 + 4 = 7 cancers.MATERIALS AND METHODSForty-seven consecutive patients with 52 PZ Gleason score 3 + 4 = 7 cancers that were 0.5 cm3 or larger underwent radical prostatectomy (RP) and 3-T MRI between 2012 and 2015. Two blinded radiologists (readers 1 and 2) retrospectively assigned PI-RADSv1 sequence (T2-weighted imaging, DWI, dynamic contrast-enhanced MRI [DCE-MRI]) and sum scores and PI-RADSv2 assessment categories. A third blinded radiologist (reader 3) measured apparent diffusion coefficient (ADC) ratio (ADC of tumor / ADC of normal PZ) using RP-MRI maps. Sensitivity, false-positive rate, and overall accuracy were compared using McNemar test. Pearson correlation was performed.RESULTSUsing PI-RADSv1, reader 1 detected 86.5% (45/52) of the cancers and reader 2, 76.9% (40/52) of the cancers. Using PI-RADSv2, reader 1 detected 78.9% (41/52) and reader 2, 67.3% (35/52). Reader 1 detected 7.7% (4/52) and reader 2 detected 9.6% (5/52) more tumors using PI-RADSv1 due to T2-weighted imaging score ≥ 4 or DCE-MRI score ≥ 3. Sensitivity was higher for PI-RADSv1 (p = 0.01 and 0.03, readers 1 and 2). False-positive rates were higher with PI-RADSv1 than with PI-RADSv2 (1.8% vs 0.9% for reader 1; 3.6% vs 1.8% for reader 2) without significant differences in false-positive rate (p = 0.41 and 0.25) or overall accuracy (p = 0.06 and 0.23). PI-RADSv1 sum scores correlated strongly with PI-RADSv2 categories (B = 0.78-0.93, p < 0.0001). The mean ADC ratio was 0.61 ± 0.14 mm2/s with no difference between visible and nonvisible tumors (p = 0.06-0.5). Interobserver agreement was moderate for PI-RADSv2 (κ = 0.41) and ranged from slight to substantial for PI-RADSv1 (T2-weighted imaging, κ = 0.32; DWI, κ = 0.52; DCE-MRI, κ = 0.13).CONCLUSIONThere was no difference in overall detection of cancers comparing PI-RADSv1 and PI-RADSv2; however, PI-RADSv1 sequence scores on T2-weighted imaging and DCE-MRI detected approximately 10% more tumors that were otherwise underestimated on DWI and using PI-RADSv2 decision-tree rules.

OBJECTIVE:The purpose of this study is to assess associations between Prostate Imaging Reporting and Data System, version 2 (PI-RADSv2), categories and the presence of a tumor with a Gleason score (GS) of 4 + 3 = 7 or greater or the presence of extraprostatic extension (EPE) at radical prostatectomy (RP) in patients with a GS 3 + 4 = 7 tumor at biopsy.MATERIALS AND METHODS:A total of 81 men with GS 3 + 4 = 7 prostate cancer diagnosed by transrectal ultrasound-guided biopsy underwent multiparametric MRI and RP between 2012 and 2015. Two blinded radiologists assessed multiparametric MR images and assigned PI-RADSv2 assessment categories (categories 1-5) with the use of sector maps, which were compared with regard to the location of the tumor, the GS, and the presence of EPE at RP. Comparisons were performed between groups with the use of chi-square and multivariate analysis. Diagnostic accuracy was assessed using ROC curve analysis, and localization was compared using the Fisher exact test.RESULTS:A total of 53.1% of men (43/81) had EPE, and 21.0% (17/81) had GS 4 + 3 = 7 prostate cancer after RP, whereas 2.5% of men (2/81) had their tumors downgraded to GS 3 + 3 = 6. No statistically significant difference in patient age, prostate specific antigen level, or clinical stage existed between groups (p > 0.05). PI-RADSv2 assessment categories were significantly higher for GS 4 + 3 = 7 tumors (p = 0.03). PI-RADSv2 showed moderate accuracy for the diagnosis of GS 4 + 3 = 7 tumors (AUC, 0.65; 95% CI, 0.54-0.77), with a category of 4 or higher having a sensitivity and specificity for diagnosis of 94.1% and 23.4%, respectively. No patient with a PI-RADSv2 category lower than 3 had a GS 4 + 3 = 7 tumor. Accuracy of tumor localization ranged from 86.4% to 92.6%, with 88.2% of errors (15/17) occurring in GS 3 + 3 = 6 or GS 3 + 4 = 7 tumors (p = 0.30). PI-RADSv2 categories were noted to be higher when EPE was present (p < 0.001). Interobserver agreement was moderate (κ = 0.43).CONCLUSION:For GS 3 + 4 = 7 cancers detected at transrectal ultrasound-guided biopsy, higher PI-RADSv2 assessment categories are associated with upgrading to GS 4 + 3 = 7 cancer and with the presence of EPE after RP. A PI-RADSv2 score of 3 or higher was 100% sensitive for diagnosing GS 4 + 3 = 7 tumors.