Bosniak Classification of Cystic Renal Masses, Version 2019: A Pictorial Guide to Clinical Use

T1-Hyperintense Cystic Renal Masses: MR Subtraction Imaging May Improve Interobserver Agreement and Diagnostic Performance in the Bosniak Classification

BACKGROUND

T1-hyperintensity in cystic renal masses (CRMs) complicates Bosniak classification assessment due to inherent signal interference from hemorrhage and proteinaceous content, potentially obscuring enhancement visibility.

PURPOSE

To investigate whether MR subtraction imaging improves interobserver agreement and diagnostic performance in the Bosniak classification of T1-hyperintense CRMs.

STUDY TYPE

Retrospective.

POPULATION

A total of 139 consecutive patients (mean age, 50 ± 12 years; 97 males) with 141 T1-hyperintense CRMs were included, consisting of surgically confirmed 133 lesions and clinically diagnosed 8 benign CRMs that were stable during follow-up (≥ 5 years).

FIELD STRENGTH/SEQUENCE

1.5/3 T. fat-saturated T2-weighted imaging, diffusion-weighted imaging, unenhanced, and triphasic dynamic contrast-enhanced T1-weighted imaging (T1WI). Subtraction images were generated automatically by subtracting unenhanced from triphasic contrast-enhanced T1WI.

ASSESSMENT

Six radiologists (half less experienced) independently classified all T1-hyperintense CRMs using the Bosniak classification (v2019) in two sessions, with and without subtraction imaging. A 1-month washout period was implemented between sessions, and the order of cases was re-randomized. Interobserver agreement and diagnostic performance were evaluated in all experienced and less experienced readers.

STATISTICAL TESTS

Weighted κ statistics assessed interobserver agreement. Diagnostic performance (the area under the curve [AUC], sensitivity, specificity) was compared using Delong and McNemar tests. Statistical significance was defined as p < 0.05.

RESULTS

Subtraction imaging significantly improved interobserver agreement in all radiologists (weighted κ = 0.62 vs. 0.46), and less experienced radiologists (3-5 years of experience, weighted κ = 0.63 vs. 0.42), though not significantly among experienced radiologists (10-15 years of experience, weighted κ = 0.61 vs. 0.52; p = 0.051). Less experienced radiologists showed significantly higher AUC (0.865 vs. 0.804), sensitivity (88.9% vs. 75.5%), and specificity (88.2% vs. 72.5%) with MR subtraction imaging.

DATA CONCLUSION

MR subtraction imaging may improve overall interobserver agreement in the Bosniak classification of T1-hyperintense CRMs. Furthermore, it could improve diagnostic accuracy and interobserver agreement among less experienced radiologists.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 2.

Diagnostic performance of contrast enhancement to differentiate benign and malignant renal lesions in CT and MRI: a systematic review and meta-analysis of diagnostic test accuracy (DTA) studies

OBJECTIVE

To perform a systematic review and meta-analysis of the diagnostic performance of contrast enhancement to differentiate benign and malignant renal lesions using CT and MRI.

MATERIAL AND METHODS

A systematic literature search of databases was performed between January 1, 1980 and September 26, 2022. We included studies reporting the accuracy of CE thresholds on CT and MRI indeterminate renal lesions, with pathologic examination and follow-up as the reference standard. Studies meeting the inclusion criteria underwent quality assessment with the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. We excluded studies with high risk of bias. Summary estimates of diagnostic performance were obtained with the bivariate Bayesian model for CT and MRI. Effects of different thresholds and index test modalities were investigated through subgroup analysis.

RESULTS

Eleven studies (1372 patients) using CT and six studies (218 patients) using MRI were included. Of the eleven studies, 15 parts from 9 studies were considered for the CT meta-analysis, and 6 parts from 3 studies for the MRI meta-analysis. Diagnostic performance meta-analysis on enhancement found a 96% summary sensitivity (95% CI 92, 98) and a 92% summary specificity (95% CI 85, 96) in 2056 renal lesions for CT; and 82% summary sensitivity (95% CI 65, 89) and an 89% summary specificity (95% CI 77, 95) in 634 lesions for MRI.

CONCLUSION

CT and MRI have high accuracy to determine enhancement and classify renal lesions, and both modalities can be used with confidence for this purpose. There are still some controversies about the optimal thresholds. Future research should evaluate outcomes and decision-making pathways to determine whether basing clinical decisions on a specific threshold on CT and MRI would do more harm than good.

Spectral Differentiation of Hyperdense Non-Vascular and Vascular Renal Lesions Without Solid Components in Contrast-Enhanced Photon-Counting Detector CT Scans-A Pilot Study

Introduction: The number of incidental renal lesions identified in CT scans of the abdomen is increasing. Objective: The aim of this study was to determine whether hyperdense renal lesions without solid components in a portal venous CT scan can be clearly classified as vascular or non-vascular by material decomposition into iodine and water. Methods: This retrospective single-center study included 26 patients (mean age 72 years ± 9; 16 male) with 42 hyperdense renal lesions (>20 HU) in a contrast-enhanced Photon-Counting Detector CT scan (PCD-CT) between May and December 2022. Spectral decomposition into virtual non-contrast (VNC) images and iodine quantification maps was performed, and HU values were quantified within the lesions. Further imaging and histopathological reports served as reference standards. Results: Mean VNC values were 55.7 (±24.2) HU for non-vascular and 32.2 (±11.1) HU for vascular renal lesions. Mean values in the iodine maps were 5.7 (±7.8) HU for non-vascular and 33.3 (±19.0) HU for vascular renal lesions. Using a threshold of >20.3 HU in iodine maps, a total of 7/8 (87.5%) vascular lesions were correctly identified. Conclusion: This proof-of-principle study suggests that the routine use of spectral information acquired in PCD-CT scans might be able to reduce the necessary workup for hyperdense renal lesions without solid components. Further studies with larger patient cohorts are necessary to validate the results of this study and to determine the usefulness of this method in clinical routine.

Clinical value of contrast-enhanced ultrasound combined with quantitative analysis in Bosniak ≥ II cystic renal masses

OBJECTIVE

The 2019 Bosniak classification has improved the precise and detailed quantitative evaluation criteria, making the diagnosis of cystic renal masses (CRMs) more accurate and objective. This study addresses the clinical value of quantitative analysis and aims to investigate the feasibility of combining contrast-enhanced ultrasound (CEUS) with quantitative analysis for diagnosing Bosniak ≥ II CRMs.

METHODS

We retrospectively obtained 58 CRMs with confirmed pathology, which underwent CEUS and Contrast-enhanced computer tomography (CECT) evaluations according to Bosniak classification between January 2013 and August 2024. These lesions were divided into benign and malignant groups, followed by a quantitative analysis of the morphological details detected by CEUS. All morphological parameters were compared, and the diagnostic efficiencies were evaluated using receiver operating characteristics (ROC) curves, logistic regression (LR) analysis, and diagnostic curve analysis (DCA). Additionally, a cohort of 72 lesions was monitored for a period of ≥ 3 years, and changes in Bosniak classification were analyzed by categorizing them into stable, upgraded, and downgraded categories.

RESULTS

The analysis revealed no statistically significant difference between CEUS and CECT in our cohort's malignancy predictive rates across different Bosniak grades (p = 0.640). All morphological quantitative parameters showed statistically significant differences between the two groups (p < 0.001). ROC curve analysis revealed that the sum of enhanced wall thickness and enhanced septum thickness among quantitative parameters had the highest AUC value (AUC: 0.9226). Both LR models demonstrated superior clinical diagnostic performance with similar level of accuracy between qualitative and quantitative analysis, as evidenced by ROC (AUC: 0.9470, 0.9619, respectively) and DCA analyses. None of the lesions in the follow-up cohort were upgraded, suggesting that CRMs are relatively stable tumors with a low malignant potential.

CONCLUSION

This retrospective study demonstrated that CEUS combined with Bosniak classification and quantitative analysis could enhance diagnostic confidence in differentiating Bosniak ≥ II CRMs and could serve as a viable alternative to CECT in specific cases.

Contrast-enhanced ultrasound in renal cystic lesions: an update

This narrative review aims to describe the current status of contrast-enhanced ultrasound (CEUS) in characterizing renal cystic lesion. The imaging techniques usually performed for their evaluation are ultrasonography (US), computed tomography (CT), and magnetic resonance imaging (MRI) with different criteria of application based on the individual case and the purpose of the examination. Generally, US, as a non-ionizing examination, is the first imaging modality performed and therefore the one that incidentally detects cystic lesions. CT is the most performed imaging modality for cystic lesion assessment before MRI evaluation. It provides better characterization and management and has been introduced into the Bosniak classification. In this context, CEUS is making its way for its characteristics and represents the emerging technique in this field. With these premises, the authors analyze the role of CEUS in the evaluation of renal cysts, starting with an explanation of the technique, describe its main advantages and limitations, and end with a discussion of its application in the Bosniak classification and management, following the current major guidelines.

To identify important MRI features to differentiate hepatic mucinous cystic neoplasms from septated hepatic cysts based on random forest

OBJECTIVE

To identify important MRI features to differentiate hepatic mucinous cystic neoplasms (MCN) from septated hepatic cysts (HC) using random forest and compared with logistic regression algorithm.

METHODS

Pathologically diagnosed hepatic cysts and hepatic MCNs with pre-operative contrast-enhanced MRI in our hospital from 2010 to 2023 were collected and only septated lesions on enhanced MRI were enrolled. A total of 21 septated HC and 18 MCNs were included in this study. Eighteen MRI features were analyzed and top important features were identified based on random forest (RF) algorithm. The results were evaluated by the prediction performance of a RF model combining the important features and compared with the performance of the logistic regression (LR) algorithm. Finally, for each identified feature, diagnostic probability, sensitivity, and specificity were calculated and compared.

RESULTS

Four variables, i.e., the septation arising from wall without indentation, multiseptate, intracapsular cyst sign, and solitary lesion were extracted as top important features with significance for MCNs by the random forest algorithm. The RF model using these variables had an AUC of 0.982 (0.95CI, 0.950-1.000), compared with the LR model based on two identified features with AUC of 0.931 (0.95CI, 0.846-1.000), p = 0.202. Among the four important features, multiseptate had the highest specificity (95.2%) and good sensitivity (72.2%, lower than the septation from wall without indentation, 94.4%) to diagnose MCNs.

CONCLUSION

Four out of 18 MRI features were extracted as reliably important factors to differ hepatic MCNs from septated HC. The combination of these four features in a RF model could achieve satisfactory diagnostic efficacy.

Localized cystic kidney disease: a case report unveiling clinical and histopathological challenges

Localized cystic kidney disease (LCKD) is a distinct renal disorder characterized by the presence of cysts within specific regions of the kidneys. We present a rare case of a 41-year-old African American man, who presented to our medical center with lower urinary tract symptoms and gross hematuria. The initial assessment culminated in the identification of an uncomplicated urinary tract infection, prompting the prescription of appropriate oral antibiotic therapy. On follow-up after 5 months, the patient presented with gross hematuria. Imaging studies revealed a mixed-density cystic lesion of 2.6 cm situated within the interpolar region of the right kidney. This cystic lesion exhibited intricate septations at the superior pole of the kidney. Robotic-assisted right partial nephrectomy was performed, and pathologic examination was diagnostic for LCKD. This report not only underscores the uniqueness of LCKD but also presents a comprehensive review of the existing literature that pertains to this condition. Particular emphasis is placed upon its inherent benign behavior and its marked divergence from the progressive trajectory commonly associated with other renal diseases. We also explored the incidental findings of the disease, its diverse clinical symptomatology, conceivable etiological underpinnings, and the array of diagnostic modalities used. Finally, similarities in histopathologic findings with polycystic kidney disease and other entities are discussed, underscoring the importance of accurate diagnosis and management.

Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT

Background Accurate characterization of suspicious small renal masses is crucial for optimized management. Deep learning (DL) algorithms may assist with this effort. Purpose To develop and validate a DL algorithm for identifying benign small renal masses at contrast-enhanced multiphase CT. Materials and Methods Surgically resected renal masses measuring 3 cm or less in diameter at contrast-enhanced CT were included. The DL algorithm was developed by using retrospective data from one hospital between 2009 and 2021, with patients randomly allocated in a training and internal test set ratio of 8:2. Between 2013 and 2021, external testing was performed on data from five independent hospitals. A prospective test set was obtained between 2021 and 2022 from one hospital. Algorithm performance was evaluated by using the area under the receiver operating characteristic curve (AUC) and compared with the results of seven clinicians using the DeLong test. Results A total of 1703 patients (mean age, 56 years ± 12 [SD]; 619 female) with a single renal mass per patient were evaluated. The retrospective data set included 1063 lesions (874 in training set, 189 internal test set); the multicenter external test set included 537 lesions (12.3%, 66 benign) with 89 subcentimeter (≤1 cm) lesions (16.6%); and the prospective test set included 103 lesions (13.6%, 14 benign) with 20 (19.4%) subcentimeter lesions. The DL algorithm performance was comparable with that of urological radiologists: for the external test set, AUC was 0.80 (95% CI: 0.75, 0.85) versus 0.84 (95% CI: 0.78, 0.88) (P = .61); for the prospective test set, AUC was 0.87 (95% CI: 0.79, 0.93) versus 0.92 (95% CI: 0.86, 0.96) (P = .70). For subcentimeter lesions in the external test set, the algorithm and urological radiologists had similar AUC of 0.74 (95% CI: 0.63, 0.83) and 0.81 (95% CI: 0.68, 0.92) (P = .78), respectively. Conclusion The multiphase CT-based DL algorithm showed comparable performance with that of radiologists for identifying benign small renal masses, including lesions of 1 cm or less. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Validation of the updated Bosniak classification (2019) in pathologically confirmed CT-categorised cysts

INTRODUCTION

The updated Bosniak classification in 2019 (v2019) addresses vague imaging terms and revises the criteria with the intent to categorise a higher proportion of cysts in lower-risk groups and reduce benign cyst resections. The aim of the present study was to compare the diagnostic accuracy and inter-observer agreement rate of the original (v2005) and updated classifications (v2019).

METHOD

Resected/biopsied cysts were categorised according to Bosniak classifications (v2005 and v2019) and the diagnostic accuracy was assessed with reference to histopathological analysis. The inter-observer agreement of v2005 and v2019 was determined.

RESULTS

The malignancy rate of the cohort was 83.6% (51/61). Using v2019, a higher proportion of malignant cysts were categorised as Bosniak ≥ III (88.2% vs 84.3%) and a significantly higher percentage were categorised as Bosniak IV (68.9% vs 47.1%; p = 0.049) in comparison to v2005. v2019 would have resulted in less benign cyst resections (13.5% vs 15.7%). Calcified versus non-calcified cysts had lower rates of malignancy (57.1% vs 91.5%; RR,0.62; p = 0.002). The inter-observer agreement of v2005 was higher than that of v2019 (kappa, 0.70 vs kappa, 0.43).

DISCUSSION

The updated classification improves the categorisation of malignant cysts and reduces benign cyst resection. The low inter-observer agreement remains a challenge to the updated classification system.

Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics

Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.

Clinical Evaluation of Cystic Renal Masses With Bosniak Classification by Contrast-Enhanced Ultrasound and Contrast-Enhanced Computer Tomography

OBJECTIVES

The study aims to compare retrospectively three clinically applied methods for the diagnostic performance of cystic renal masses (CRMs) by contrast-enhanced ultrasound (CEUS) and contrast-enhanced computer tomography (CECT) with Bosniak classification system.

METHODS

A total of 52 cases of Bosniak II-IV CRMs in 49 consecutive patients were diagnosed from January 2013 to July 2022 and their data were analyzed. All patients had been subjected to CEUS and CECT simultaneously. Pathological diagnoses and masses stability were used as standard references to determine whether lesions were malignant or benign. Then 49 CRMs only with pathologic results were classified into group 1 and 2.

RESULTS

A total of 52 CRMs in 49 enrolled patients were classified into 8 category II, 16 category IIF, 15 category III, and 13 category IV by CEUS (EFSUMB 2020), 10 category II, 13 category IIF, 16 category III, and 13 category IV by CEUS (V2019), while 15 category II, 9 category IIF, 13 category III, and 15 category IV by CECT (V2019). Pathological results and masses stability longer than 5 years follow-up performed substantially for CEUS (EFSUMB 2020), CEUS (V2019), and CECT (V2019) (kappa values were 0.696, 0.735, and 0.696, respectively). Among 49 pathologic approving CRMs, wall/septation thickness ≥4 mm, wall/septation thickness, presence of enhancing nodule and the diameter were found to be statistically significant for malignancy. Twenty-two malignant masses were correctly diagnosed by CEUS (V2019), while 21 malignant masses were both correctly diagnosed by CEUS (EFSUMB 2020) and CECT (V2019), and 1 mass was misdiagnosed.

CONCLUSIONS

Bosniak classification of EFSUMB 2020 version might be as accurate as version 2019 CEUS and version 2019 CECT in diagnosing CRMs, and CEUS is found to have an excellent safety profile in dealing with clinical works.

Role of magnetic resonance imaging in characterization of cystic renal lesions based on Bosniak classification version 2019

Background

In 2019, the Bosniak classification system for cystic renal lesions underwent modifications aimed at addressing the limitations of the original classification.

Results

The revised 2019 version demonstrated notable differences from its predecessor. Specifically, it showed an increased proportion of class IIF cystic lesions (31% compared to 16.7%) and a decreased proportion of class III cystic lesions (27.4% compared to 45.2%). Additionally, the malignancy rate for class III cystic renal lesions was lower in the 2019 version (37.8% vs. 42.2%). When it came to diagnosing malignancies, the 2019 version exhibited higher specificity (74.4% compared to 41.03%) while maintaining a comparable sensitivity (97.8% vs. 100%) compared to the original Bosniak system.

Conclusions

The Bosniak 2019 version demonstrated enhanced specificity and diagnostic accuracy for malignancies in comparison to the original Bosniak system, all while maintaining an equivalent sensitivity.

Renal Mass Imaging with MRI Clear Cell Likelihood Score: A User's Guide

Small solid renal masses (SRMs) are frequently detected at imaging. Nearly 20% are benign, making careful evaluation with MRI an important consideration before deciding on management. Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma subtype with potentially aggressive behavior. Thus, confident identification of ccRCC imaging features is a critical task for the radiologist. Imaging features distinguishing ccRCC from other benign and malignant renal masses are based on major features (T2 signal intensity, corticomedullary phase enhancement, and the presence of microscopic fat) and ancillary features (segmental enhancement inversion, arterial-to-delayed enhancement ratio, and diffusion restriction). The clear cell likelihood score (ccLS) system was recently devised to provide a standardized framework for categorizing SRMs, offering a Likert score of the likelihood of ccRCC ranging from 1 (very unlikely) to 5 (very likely). Alternative diagnoses based on imaging appearance are also suggested by the algorithm. Furthermore, the ccLS system aims to stratify which patients may or may not benefit from biopsy. The authors use case examples to guide the reader through the evaluation of major and ancillary MRI features of the ccLS algorithm for assigning a likelihood score to an SRM. The authors also discuss patient selection, imaging parameters, pitfalls, and areas for future development. The goal is for radiologists to be better equipped to guide management and improve shared decision making between the patient and treating physician. © RSNA, 2023 Quiz questions for this article are available in the supplemental material. See the invited commentary by Pedrosa in this issue.

Bosniak classification version 2019: a prospective comparison of CT and MRI

OBJECTIVE

To assess the diagnostic accuracy and agreement of CT and MRI in terms of the Bosniak classification version 2019 (BCv2019).

MATERIALS AND METHODS

A prospective multi-institutional study enrolled 63 patients with 67 complicated cystic renal masses (CRMs) discovered during ultrasound examination. All patients underwent CT and MRI scans and histopathology. Three radiologists independently assessed CRMs using BCv2019 and assigned Bosniak class to each CRM using CT and MRI. The final analysis included 60 histopathologically confirmed CRMs (41 were malignant and 19 were benign).

RESULTS

Discordance between CT and MRI findings was noticed in 50% (30/60) CRMs when data were analyzed in terms of the Bosniak classes. Of these, 16 (53.3%) were malignant. Based on consensus reviewing, there was no difference in the sensitivity, specificity, and accuracy of the BCv2019 with MRI and BCv2019 with CT (87.8%; 95% CI = 73.8-95.9% versus 75.6%; 95% CI = 59.7-87.6%; p = 0.09, 84.2%; 95% CI = 60.4-96.6% versus 78.9%; 95% CI = 54.4-93.9%; p = 0.5, and 86.7%; 95% CI = 64.0-86.6% versus 76.7%; 95% CI = 75.4-94.1%; p = 0.1, respectively). The number and thickness of septa and the presence of enhanced nodules accounted for the majority of variations in Bosniak classes between CT and MRI. The inter-reader agreement (IRA) was substantial for determining the Bosniak class in CT and MRI (k = 0.66; 95% CI = 0.54-0.76, k = 0.62; 95% CI = 0.50-0.73, respectively). The inter-modality agreement of the BCv219 between CT and MRI was moderate (κ = 0.58).

CONCLUSION

In terms of BCv2019, CT and MRI are comparable in the classification of CRMs with no significant difference in diagnostic accuracy and reliability.

KEY POINTS

• There is no significant difference in the sensitivity, specificity, and accuracy of the BCv2019 with MRI and BCv2019 with CT. • The number of septa and their thickness and the presence of enhanced nodules accounted for the majority of variations in Bosniak classes between CT and MRI. • The inter-reader agreement was substantial for determining the Bosniak class in CT and MRI and the inter-modality agreement of the BCv219 between CT and MRI was moderate.

The thin line that made the difference: a case report on a Bosniak IIF renal cystic mass treated with cyst decortication

Background

Among all benign kidney lesions, renal cysts are the most common type. In the proposed update of 2019, the Bosniak classification of cystic renal masses is used to classify renal masses according to their likelihood of malignancy, both on computed tomography (CT) and on magnetic resonance imaging (MRI).

Case presentation

A middle-aged Caucasian male presented to our department with chronic right flank pain. Imaging studies revealed a right renal Bosniak IIF cyst, later complicated by traumatic haemorrhage. The patient consequently underwent cyst decortication for symptom relief. Biopsy results from samples taken during the laparoscopic operation revealed ISUP grade 1 cystic clear cell carcinoma.

Conclusion

The treatment of Bosniak IIF cysts has long been a matter of debate. As a result of scarcity of data on the probability of malignancy in MRI using the new classification, such cysts should be carefully scrutinised and staged before choosing a treatment option. Retroperitoneal seeding should always be considered in interventions involving an incomplete resection margin or cyst drainage.

Diagnostic performance of the Bosniak classification, version 2019 for cystic renal masses: A systematic review and meta-analysis

Purpose

To systematically assess the diagnostic performance of the Bosniak classification, version 2019 for risk stratification of cystic renal masses.

Methods

We conducted an electronic literature search on Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar to identify relevant articles between June 1, 2019 and March 31, 2022 that used the Bosniak classification, version 2019 for risk stratification of cystic renal masses. Summary estimates of sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−), and diagnostic odds ratio (DOR) were pooled with the bivariate model and hierarchical summary receiver operating characteristic (HSROC) model. The quality of the included studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

Results

A total of eight studies comprising 720 patients were included. The pooled sensitivity and specificity were 0.85 (95% CI 0.79–0.90) and 0.68 (95% CI 0.58–0.76), respectively, for the class III/IV threshold, with a calculated area under the HSROC curve of 0.84 (95% CI 0.81–0.87). The pooled LR+, LR−, and DOR were 2.62 (95% CI 2.0–3.44), 0.22 (95% CI 0.16–0.32), and 11.7 (95% CI 6.8–20.0), respectively. The Higgins I² statistics demonstrated substantial heterogeneity across studies, with an I² of 57.8% for sensitivity and an I² of 74.6% for specificity. In subgroup analyses, the pooled sensitivity and specificity for CT were 0.86 and 0.71, respectively, and those for MRI were 0.87 and 0.67, respectively. In five studies providing a head-to-head comparison between the two versions of the Bosniak classification, the 2019 version demonstrated significantly higher specificity (0.62 vs. 0.41, p < 0.001); however, it came at the cost of a significant decrease in sensitivity (0.88 vs. 0.94, p = 0.001).

Conclusions

The Bosniak classification, version 2019 demonstrated moderate sensitivity and specificity, and there was no difference in diagnostic accuracy between CT and MRI. Compared to version 2005, the Bosniak classification, version 2019 has the potential to significantly reduce overtreatment, but at the cost of a substantial decline in sensitivity.

Renal Lesion Characterization by Dual-Layer Dual-Energy CT: Comparison of Virtual and True Unenhanced Images

BACKGROUND. Prior studies have provided mixed results for the ability to replace true unenhanced (TUE) images with virtual unenhanced (VUE) images when characterizing renal lesions by dual-energy CT (DECT). Detector-based dual-layer DECT (dlDECT) systems may optimize performance of VUE images for this purpose. OBJECTIVE. The purpose of this article was to compare dual-phase dlDECT examinations evaluated using VUE and TUE images in differentiating cystic and solid renal masses. METHODS. This retrospective study included 110 patients (mean age, 64.3 ± 11.8 years; 46 women, 64 men) who underwent renal-mass protocol dlDECT between July 2018 and February 2022. TUE, VUE, and nephrographic phase image sets were reconstructed. Lesions were diagnosed as solid masses by histopathology or MRI. Lesions were diagnosed as cysts by composite criteria reflecting findings from MRI, ultrasound, and the TUE and nephrographic phase images of the dlDECT examinations. One radiologist measured lesions' attenuation on all dlDECT image sets. Lesion characterization was compared between use of VUE and TUE images, including when considering enhancement of 20 HU or greater to indicate presence of a solid mass. RESULTS. The analysis included 219 lesions (33 solid masses; 186 cysts [132 simple, 20 septate, 34 hyperattenuating]). TUE and VUE attenuation were significantly different for solid masses (33.4 ± 7.1 HU vs 35.4 ± 8.6 HU, p = .002), simple cysts (10.8 ± 5.6 HU vs 7.1 ± 8.1 HU, p < .001), and hyperattenuating cysts (56.3 ± 21.0 HU vs 47.6 ± 16.3 HU, p < .001), but not septate cysts (13.6 ± 8.1 HU vs 14.0 ± 6.8 HU, p = .79). Frequency of enhancement 20 HU or greater when using TUE and VUE images was 90.9% and 90.9% in solid masses, 0.0% and 9.1% in simple cysts, 15.0% and 10.0% in septate cysts, and 11.8% and 38.2% in hyperattenuating cysts. All solid lesions were concordant in terms of enhancement 20 HU or greater when using TUE and VUE images. Twelve simple cysts and nine hyperattenuating cysts showed enhancement of 20 HU or greater when using VUE but not TUE images. CONCLUSION. Use of VUE images reliably detected enhancement in solid masses. However, VUE images underestimated attenuation of simple and hyperattenuating cysts, leading to false-positive findings of enhancement by such lesions. CLINICAL IMPACT. The findings do not support replacement of TUE acquisitions with VUE images when characterizing renal lesions by dlDECT.