Bosniak Classification of Cystic Renal Masses Version 2019: Comparison to Version 2005 for Class Distribution, Diagnostic Performance, and Interreader Agreement Using CT and MRI

Bosniak Classification of Cystic Renal Masses: Looking Back, Looking Forward

RATIONALE AND OBJECTIVES

According to the 2019 update of the Bosniak classification, the main imaging features that need to be evaluated to achieve a correct characterization of renal cystic masses include the thickness of walls and septa, the number of septa, the appearance of walls and septa, the attenuation/intensity on non-contrast CT/MRI and the presence of unequivocally perceived or measurable enhancement of walls and septa. Despite the improvement deriving from a quantitative evaluation of imaging features, certain limitations seem to persist and some possible scenarios that can be encountered in clinical practice are still missing.

MATERIALS AND METHODS

A deep analysis of the 2019 update of the Bosniak classification was performed.

RESULTS

The most notable potential flaws concern: (1) the quantitative measurement of the walls and septa; (2) the fact that walls and septa > 2 mm are always referred to as "enhancing", not considering the alternative scenario; (3) the description of some class II masses partially overlaps with each other and with the definition of class I masses and (4) the morphological variations of cystic masses over time is not considered.

CONCLUSION

The present paper analyzes in detail the limitations of the 2019 Bosniak classification to improve this important tool and facilitate its use in daily radiological practice.

Update on Renal Cell Carcinoma Diagnosis with Novel Imaging Approaches

This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer.

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.

Insights into Renal Cell Carcinoma with Novel Imaging Approaches

This article presents a comprehensive overview of new imaging approaches and techniques for improving the assessment of renal masses and renal cell carcinoma. The Bosniak classification, version 2019, as well as the clear cell likelihood score, version 2.0, will be discussed as new imaging algorithms using established techniques. Additionally, newer modalities, such as contrast-enhanced ultrasound, dual energy computed tomography, and molecular imaging, will be discussed in conjunction with emerging radiomics and artificial intelligence techniques. Current diagnostic algorithms combined with newer approaches may be an effective way to overcome existing limitations in renal mass and RCC characterization.

CEUS Bosniak Classification-Time for Differentiation and Change in Renal Cyst Surveillance

It is time for a change. CEUS is an established method that should be much more actively included in renal cyst monitoring strategies. This review compares the accuracies, strengths, and weaknesses of CEUS, CECT, and MRI in the classification of renal cysts. In order to avoid overstaging by CEUS, a further differentiation of classes IIF, III, and IV is required. A further development in the refinement of the CEUS-Bosniak classification aims to integrate CEUS more closely into the monitoring of renal cysts and to develop new and complex monitoring algorithms.

A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging

Genitourinary (GU) system is among the most commonly involved malignancy sites in the human body. Imaging plays a crucial role not only in diagnosis of cancer but also in disease management and its prognosis. However, interpretation of conventional imaging methods such as CT or MR imaging (MRI) usually demonstrates variability across different readers and institutions. Artificial intelligence (AI) has emerged as a promising technology that could improve the patient care by providing helpful input to human readers through lesion detection algorithms and lesion classification systems. Moreover, the robustness of these models may be valuable in automating time-consuming tasks such as organ and lesion segmentations. Herein, we review the current state of imaging and existing challenges in GU malignancies, particularly for cancers of prostate, kidney and bladder; and briefly summarize the recent AI-based solutions to these challenges.

Differentiating Benign From Malignant Cystic Renal Masses: A Feasibility Study of Computed Tomography Texture-Based Machine Learning Algorithms

OBJECTIVE

The Bosniak classification attempts to predict the likelihood of renal cell carcinoma (RCC) among cystic renal masses but is subject to interobserver variability and often requires multiphase imaging. Artificial intelligence may provide a more objective assessment. We applied computed tomography texture-based machine learning algorithms to differentiate benign from malignant cystic renal masses.

METHODS

This is an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study of 147 patients (mean age, 62.4 years; range, 28-89 years; 94 men) with 144 cystic renal masses (93 benign, 51 RCC); 69 were pathology proven (51 RCC, 18 benign), and 75 were considered benign based on more than 4 years of stability at follow-up imaging. Using a single image from a contrast-enhanced abdominal computed tomography scan, mean, SD, mean value of positive pixels, entropy, skewness, and kurtosis radiomics features were extracted. Random forest, multivariate logistic regression, and support vector machine models were used to classify each mass as benign or malignant with 10-fold cross validation. Receiver operating characteristic curves assessed algorithm performance in the aggregated test data.

RESULTS

For the detection of malignancy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve were 0.61, 0.87, 0.72, 0.80, and 0.79 for the random forest model; 0.59, 0.87, 0.71, 0.79, and 0.80 for the logistic regression model; and 0.55, 0.86, 0.68, 0.78, and 0.76 for the support vector machine model.

CONCLUSION

Computed tomography texture-based machine learning algorithms show promise in differentiating benign from malignant cystic renal masses. Once validated, these may serve as an adjunct to radiologists' assessments.

CT-based Bosniak classification of cystic renal lesions: is version 2019 an improvement on version 2005?

OBJECTIVE

To compare the diagnostic performance and inter-reader agreement of the CT-based v2019 versus v2005 Bosniak classification systems for risk stratification of cystic renal lesions (CRL).

METHODS

This retrospective study included adult patients with CRL identified on CT scan between 2005 and 2018. The reference standard was histopathology or a minimum 4-year imaging follow-up. The studies were reviewed independently by five readers (three senior, two junior), blinded to pathology results and imaging follow-up, who assigned Bosniak categories based on the 2005 and 2019 versions. Diagnostic performance of v2005 and v2019 Bosniak classifications for distinguishing benign from malignant lesions was calculated by dichotomizing CRL into the potential for ablative therapy (III-IV) or conservative management (I-IIF). Inter-reader agreement was calculated using Light's Kappa.

RESULTS

One hundred thirty-nine patients with 149 CRL (33 malignant) were included. v2005 and v2019 Bosniak classifications achieved similar diagnostic performance with a sensitivity of 91% vs 91% and a specificity of 89% vs 88%, respectively. Inter-reader agreement for overall Bosniak category assignment was substantial for v2005 (κ = 0.78) and v2019 (κ = 0.75) between senior readers but decreased for v2019 when the Bosniak classification was dichotomized to conservative management (I-IIF) or ablative therapy (III-IV) (0.80 vs 0.71, respectively). For v2019, wall thickness was the morphological feature with the poorest inter-reader agreement (κ = 0.43 and 0.18 for senior and junior readers, respectively).

CONCLUSION

No significant improvement in diagnostic performance and inter-reader agreement was shown between v2005 and v2019. The observed decrease in inter-reader agreement in v2019 when dichotomized according to management strategy may reflect the more stringent morphological criteria.

KEY POINTS

• Versions 2005 and 2019 Bosniak classifications achieved similar diagnostic performance, but the specificity of higher risk categories (III and IV) was not increased while one malignant lesion was downgraded to v2019 Bosniak category II (i.e., not subjected to further follow-up). • Inter-reader agreement was similar between v2005 and v2019 but moderately decreased for v2019 when the Bosniak classification was dichotomized according to the potential need for ablative therapies (I-II-IIF vs III-IV).

Proportion of malignancy in Bosniak classification of cystic renal masses version 2019 (v2019) classes: systematic review and meta-analysis

OBJECTIVES

Determine the proportion of malignancy within Bosniak v2019 classes.

METHODS

MEDLINE and EMBASE were searched. Eligible studies contained patients with cystic renal masses undergoing CT or MRI renal protocol examinations with pathology confirmation, applying Bosniak v2019. Proportion of malignancy was estimated within Bosniak v2019 class. Risk of bias was assessed using QUADAS-2.

RESULTS

We included 471 patients with 480 cystic renal masses. No class I malignant masses were observed. Pooled proportion of malignancy were class II, 12% (6/51, 95% CI 5-24%); class IIF, 46% (37/85, 95% CI 28-66%); class III, 79% (138/173, 95% CI 68-88%); and class IV, 84% (114/135, 95% CI 77-90%). Proportion of malignancy differed between Bosniak v2019 II-IV classes (p = 0.004). Four studies reported the proportion of malignancy by wall/septa feature. The pooled proportion of malignancy with 95% CI were class III thick smooth wall/septa, 77% (41/56, 95% CI 53-91%); class III obtuse protrusion ≤ 3 mm (irregularity), 83% (97/117, 95% CI 75-89%); and class IV nodule with acute angulation, 86% (50/58, 95% CI 75-93%) or obtuse angulation ≥ 4 mm, 83%, (64/77, 95% CI 73-90%). Subgroup analysis by wall/septa feature was limited by sample size; however, no differences were found comparing class III masses with irregularity to class IV masses (p = 0.74) or between class IV masses by acute versus obtuse angles (p = 0.62).

CONCLUSION

Preliminary data suggest Bosniak v2019 class IIF masses have higher proportion of malignancy compared to the original classification, controlling for pathologic reference standard. There are no differences in proportion of malignancy comparing class III masses with irregularities to class IV masses with acute or obtuse nodules.

KEY POINTS

• The proportion of malignancy in Bosniak v2019 class IIF cystic masses is 46% (37 malignant/85 total IIF masses, 95% confidence intervals (CI) 28-66%). • The proportion of malignancy in Bosniak v2019 class III cystic masses is 79% (138/173, 95% CI 68-88%) and in Bosniak v2019 class IV cystic masses is 84% (114/135, 95% CI 77-90%). • Class III cystic masses with irregularities had similar proportion of malignancy (83%, 97/117, 95% CI 75-89%) compared to Bosniak class IV masses (84%, 114/135, 95% CI 77-90%) overall (p = 0.74) with no difference within class IV masses by acute versus obtuse angulation (p = 0.62).

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.

Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions

BACKGROUND

The rising prevalence of cystic renal lesions (CRLs) detected by computed tomography necessitates better identification of the malignant cystic renal neoplasms since a significant majority of CRLs are benign renal cysts. Using arterial phase CT scans combined with pathology diagnosis results, a fusion feature-based blending ensemble machine learning model was created to identify malignant renal neoplasms from cystic renal lesions (CRLs). Histopathology results were adopted as diagnosis standard. Pretrained 3D-ResNet50 network was selected for non-handcrafted features extraction and pyradiomics toolbox was selected for handcrafted features extraction. Tenfold cross validated least absolute shrinkage and selection operator regression methods were selected to identify the most discriminative candidate features in the development cohort. Feature's reproducibility was evaluated by intra-class correlation coefficients and inter-class correlation coefficients. Pearson correlation coefficients for normal distribution and Spearman's rank correlation coefficients for non-normal distribution were utilized to remove redundant features. After that, a blending ensemble machine learning model were developed in training cohort. Area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA) were employed to evaluate the performance of the final model in testing cohort.

RESULTS

The fusion feature-based machine learning algorithm demonstrated excellent diagnostic performance in external validation dataset (AUC = 0.934, ACC = 0.905). Net benefits presented by DCA are higher than Bosniak-2019 version classification for stratifying patients with CRL to the appropriate surgery procedure.

CONCLUSIONS

Fusion feature-based classifier accurately distinguished malignant and benign CRLs which outperformed the Bosniak-2019 version classification and illustrated improved clinical decision-making utility.

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.

Growth Kinetics and Progression Rate of Bosniak Classification, Version 2019 III and IV Cystic Renal Masses on Imaging Surveillance

Background: Active surveillance is increasingly used as first-line management for localized renal masses. Triggers for intervention primarily reflect growth kinetics, which are poorly investigated for cystic masses defined by Bosniak classification version 2019 (v2019). Objective: To determine growth kinetics and incidence rates of progression of class III and IV cystic renal masses, as defined by Bosniak classification v2019. Methods: This retrospective study included 105 patients (68 men, 37 women; median age, 67 years) with 112 Bosniak v2019 class III or IV cystic renal masses on baseline renal-mass protocol CT or MRI examinations from January 2005 to September 2021. Mass dimensions were measured. Progression was defined as any of: linear growth rate (LGR) ≥5 mm per year (representing clinical guideline threshold for intervention), volume doubling time <1 year, T category increase, or N1 or M1 disease. Class III and IV masses were compared. Time-to-progression was estimated using Kaplan-Meier curve analysis. Results: At baseline, 58 masses were class III and 54 were class IV. Median follow-up was 406 days. Median LGR was for class III masses 0.0 mm per year [interquartile range (IQR) -1.3 to 1.8] and for class IV masses 2.3 mm per year (IQR 0.0¬¬-5.7) (p<.001). LGR exceeded 5 mm per year in 4 (7%) class 3 masses and 15 (28%) class IV masses (p=.005). Two patients, both with class IV masses, developed distant metastases. Incidence rate of progression was for class III masses 11.0 (95% CI 4.5-22.8) and for class IV masses 73.6 (95% CI 47.8-108.7) per 100,000 person-days of follow-up. Median time-to-progression was undefined for class III mases given small number of progression events and 710 days for class IV masses. Hazard ratio of progression for class IV relative to class III masses was 5.1 (95% CI 2.5-10.8) (p<.001). Conclusion: During active surveillance of cystic masses evaluated using Bosniak classification v2019, class IV masses grew faster and were more likely to progress than class III masses. Clinical Impact: In comparison with current active surveillance guidelines that treat class III and IV masses similarly, future iterations may incorporate relatively more intensive surveillance for class IV masses.