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

Bosniak Classification of Cystic Renal Masses Version 2019: Proportion of Malignancy by Class and Subclass-Systematic Review and Meta-Analysis

Background: Bosniak classification version 2019 (v2019) was a major revision to version 2005 (v2005) that defined cystic renal mass subclasses based on wall or septa features. Objective: 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 v2019, and had a reference standard (histopathology indicating benignity or malignancy or ≥5-year imaging follow-up indicating benignity). 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: II, 9% (95% CI: 5-17%); IIF, 26% (95% CI: 13-46%); III, 80% (95% CI: 71-87%); IV, 88% (95% CI: 83-91%). Pooled proportions of malignancy by subclass were: 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-weighted hyperintensity, 26% (95% CI: 8-57%); III with thick smooth wall or septa, 78% (95% CI: 60-90%); III with obtuse protrusion(s) ≤3 mm, 84% (95% CI: 77-90%); IV with acute protrusion(s) of any size, 88% (95% CI: 80-93%); IV with obtuse protrusion(s) ≥4 mm, 86% (95% CI: 77-91%). 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, proportion of malignancy was: IIF, 24% versus 42% (p=.13); III, 74% versus 77% (p=.72); 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.

Increased prevalence of kidney cysts in individuals carrying heterozygous COL4A3 or COL4A4 pathogenic variants

BACKGROUND

Clinical variability among individuals with heterozygous pathogenic/likely pathogenic (P/LP) variants in the COL4A3/COL4A4 genes (also called autosomal dominant Alport syndrome or COL4A3/COL4A4-related disorder) is huge; many individuals are asymptomatic or show microhematuria, while others may develop proteinuria and chronic kidney disease (CKD). The prevalence of simple kidney cysts (KC) in the general population varies according to age, and patients with advanced CKD are prone to have them. A possible association between heterozygous COL4A3, COL4A4 and COL4A5 P/LP variants and KC has been described in small cohorts. The presence of KC in a multicenter cohort of individuals with heterozygous P/LP variants in the COL4A3/COL4A4 genes is assessed in this study.

METHODS

We evaluated the presence of KC by ultrasound in 157 individuals with P/LP variants in COL4A3 (40.7%) or COL4A4 (53.5%) without kidney replacement therapy. The association between presence of KC and age, proteinuria, estimated glomerular filtration rate (eGFR) and causative gene was analyzed. Prevalence of KC was compared with historical case series in the general population.

RESULTS

Half of the individuals with P/LP variants in COL4A3/COL4A4 showed KC, which is a significantly higher percentage than in the general population. Only 3.8% (6/157) had cystic nephromegaly. Age and eGFR showed an association with the presence of KC (P < .001). No association was found between KC and proteinuria, sex or causative gene.

CONCLUSIONS

Individuals with COL4A3/COL4A4 P/LP variants are prone to develop KC more frequently than the general population, and their presence is related to age and to eGFR. Neither proteinuria, sex nor the causative gene influences the presence of KC in these individuals.

Deep learning system for malignancy risk prediction in cystic renal lesions: a multicenter study

OBJECTIVES

To develop an interactive, non-invasive artificial intelligence (AI) system for malignancy risk prediction in cystic renal lesions (CRLs).

METHODS

In this retrospective, multicenter diagnostic study, we evaluated 715 patients. An interactive geodesic-based 3D segmentation model was created for CRLs segmentation. A CRLs classification model was developed using spatial encoder temporal decoder (SETD) architecture. The classification model combines a 3D-ResNet50 network for extracting spatial features and a gated recurrent unit (GRU) network for decoding temporal features from multi-phase CT images. We assessed the segmentation model using sensitivity (SEN), specificity (SPE), intersection over union (IOU), and dice similarity (Dice) metrics. The classification model's performance was evaluated using the area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA).

RESULTS

From 2012 to 2023, we included 477 CRLs (median age, 57 [IQR: 48-65]; 173 men) in the training cohort, 226 CRLs (median age, 60 [IQR: 52-69]; 77 men) in the validation cohort, and 239 CRLs (median age, 59 [IQR: 53-69]; 95 men) in the testing cohort (external validation cohort 1, cohort 2, and cohort 3). The segmentation model and SETD classifier exhibited excellent performance in both validation (AUC = 0.973, ACC = 0.916, Dice = 0.847, IOU = 0.743, SEN = 0.840, SPE = 1.000) and testing datasets (AUC = 0.998, ACC = 0.988, Dice = 0.861, IOU = 0.762, SEN = 0.876, SPE = 1.000).

CONCLUSION

The AI system demonstrated excellent benign-malignant discriminatory ability across both validation and testing datasets and illustrated improved clinical decision-making utility.

CRITICAL RELEVANCE STATEMENT

In this era when incidental CRLs are prevalent, this interactive, non-invasive AI system will facilitate accurate diagnosis of CRLs, reducing excessive follow-up and overtreatment.

KEY POINTS

The rising prevalence of CRLs necessitates better malignancy prediction strategies. The AI system demonstrated excellent diagnostic performance in identifying malignant CRL. The AI system illustrated improved clinical decision-making utility.

Applicability of Bosniak 2019 for renal mass classification on portal venous phase at the era of spectral CT imaging using rapid kV-switching dual-energy CT

OBJECTIVES

To evaluate the applicability of Bosniak 2019 criteria on a monophasic portal venous phase using rapid kilovoltage-switching DECT (rsDECT).

MATERIALS AND METHODS

One hundred twenty-seven renal masses assessed on rsDECT were included, classified according to Bosniak 2019 classification using MRI as the reference standard. Using the portal venous phase, virtual monochromatic images at 40, 50, and 77 keV; virtual unenhanced (VUE) images; and iodine map images were reconstructed. Changes in attenuation values between VUE and 40 keV, 50 keV, and 77 keV measurements were computed and respectively defined as ∆HU40keV, ∆HU50keV, and ∆HU77keV. The values of ∆HU40keV, ∆HU50keV, and ∆HU77keV thresholds providing the optimal diagnostic performance for the detection of internal enhancement were determined using Youden index.

RESULTS

Population study included 25 solid renal masses (25/127, 20%) and 102 cystic renal masses (102/127, 80%). To differentiate solid to cystic masses, the specificity of the predefined 20 HU threshold reached 88% (95%CI: 82, 93) using ∆HU77keV and 21% (95%CI: 15, 28) using ∆HU40keV. The estimated optimal threshold of attenuation change was 19 HU on ∆HU77keV, 69 HU on ∆HU50eV, and 111 HU on ∆HU40eV. The rsDECT classification was highly similar to that of MRI for solid renal masses (23/25, 92%) and for Bosniak 1 masses (62/66, 94%). However, 2 hyperattenuating Bosniak 2 renal masses (2/26, 8%) were classified as solid renal masses on rsDECT.

CONCLUSION

DECT is a promising tool for Bosniak classification particularly to differentiate solid from Bosniak I-II cyst. However, known enhancement thresholds must be adapted especially to the energy level of virtual monochromatic reconstructions.

CLINICAL STATEMENT

DECT is a promising tool for Bosniak classification; however, known enhancement thresholds must be adapted according to the types of reconstructions used and especially to the energy level of virtual monochromatic reconstructions.

KEY POINTS

• To differentiate solid to cystic renal masses, predefined 20 HU threshold had a poor specificity using 40 keV virtual monochromatic images. • Most of Bosniak 1 masses according to MRI were also classified as Bosniak 1 on rapid kV-switching dual-energy CT (rsDECT). • Bosniak 2 hyperattenuating renal cysts mimicked solid lesion on rsDECT.

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.

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.