Bosniak classification of cystic renal masses version 2019 does not increase the interobserver agreement or the proportion of masses categorized into lower Bosniak classes for non-subspecialized readers on CT or MR

Pacheco, Eduardo Oliveira; Torres, Ulysses S.; Alves, Aldo Maurici Araújo; Bekhor, Daniel; D'Ippólito, Giuseppe

doi:10.1016/j.ejrad.2020.109270

Cited by 28 publications

(13 citation statements)

References 27 publications

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“…However, the majority of the studies did not observe a substantial improvement in inter-reader agreement in the Bosniak classification, version 2019, suggesting that the updates to the classification system have not solved the previously identified issues with inter-reader variability. Moreover, one study found that the inter-reader agreement of the Bosniak classification, version 2019 was lower than that of version 2005, both for CT and MRI ( 32 ). Thus, efforts still need to be made to standardize imaging interpretations and improve inter-reader agreement in the future.…”

Section: Discussionmentioning

confidence: 99%

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

Zhang

Dai

2022

Front. Oncol.

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PurposeTo systematically assess the diagnostic performance of the Bosniak classification, version 2019 for risk stratification of cystic renal masses.MethodsWe 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.ResultsA 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).ConclusionsThe 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.

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Section: Discussionmentioning

confidence: 99%

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

Zhang

Dai

2022

Front. Oncol.

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“…The updated 2019 version of the Bosniak classification intends to address inter-reader variability and improve diagnostic performance in predicting malignancy CRL. However, the proposed classification ability has yet to be confirmed ( 22 ). In this study, two different well-trained readers still disagreed on some CRL issues.…”

Section: Discussionmentioning

confidence: 99%

Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm

Tan

Liao

et al. 2022

Front. Oncol.

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Using nephrographic phase CT images combined with pathology diagnosis, we aim to develop and validate a fusion feature-based stacking ensemble machine learning model to distinguish malignant renal neoplasms from cystic renal lesions (CRLs). This retrospective research includes 166 individuals with CRLs for model training and 47 individuals with CRLs in another institution for model testing. Histopathology results are adopted as diagnosis criterion. Nephrographic phase CT scans are selected to build the fusion feature-based machine learning algorithms. The pretrained 3D-ResNet50 CNN model and radiomics methods are selected to extract deep features and radiomics features, respectively. Fivefold cross-validated least absolute shrinkage and selection operator (LASSO) regression methods are adopted to identify the most discriminative candidate features in the development cohort. Intraclass correlation coefficients and interclass correlation coefficients are employed to evaluate feature’s reproducibility. Pearson correlation coefficients for normal distribution features and Spearman’s rank correlation coefficients for non-normal distribution features are used to eliminate redundant features. After that, stacking ensemble machine learning models are developed in the training cohort. The area under the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) are adopted in the testing cohort to evaluate the performance of each model. The stacking ensemble machine learning algorithm reached excellent diagnostic performance in the testing dataset. The calibration plot shows good stability when using the stacking ensemble model. Net benefits presented by DCA are higher than the Bosniak 2019 version classification when employing any machine learning algorithm. The fusion feature-based machine learning algorithm accurately distinguishes malignant renal neoplasms from CRLs, which outperformed the Bosniak 2019 version classification, and proves to be more applicable for clinical decision-making.

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“…It was remarkable that the 2019 version formally incorporated MRI into the Bosniak system. According to several recent MRI-related prospective studies, it remained kind of controversy about the improvement of the interobserver agreement or the reduction of the mean category with the 2019 version [24,25,29]. More studies are warranted to verify the diagnostic efficiency of the new version with different imaging techniques.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Values of CEUS, CECT and CEMRI for Renal Cystic Lesions on the Current Bosniak Criterion-A Meta-analysi

Yang¹,

Yang²,

He³

2022

ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY

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Objective: CT-based Bosniak classification system has been routinely used to assess complex renal cystic lesions and also been applied to contrast-enhanced magnetic resonance imaging (CEMRI) and contrast-enhanced ultrasound (CEUS). Besides, the 2019 new version incorporated MRI into the Bosniak system. However, the role of US in the Bosniak system has not been clearly established. The aim of this study was to compare the diagnostic ability of CEUS, CECT and CEMRI for renal cystic lesions based on the current Bosniak classification.Methods: Related studies were searched in PubMed, EMBASE, and the Cochrane Library databases from January 1, 2010 to December 14, 2020. QUADAS-2 was used to assess the study quality. Meta-analysis was performed by "midas modules" of Stata SE 15.0 software. The bivariate mixed-effect model was used. The pooled sensitivity and specificity of these three modalities were calculated and compared. Meta-regression and subgroup analyses were conducted to reveal the source of heterogeneity.

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Bosniak classification of cystic renal masses version 2019 does not increase the interobserver agreement or the proportion of masses categorized into lower Bosniak classes for non-subspecialized readers on CT or MR

Cited by 28 publications

References 27 publications

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

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

Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm

Diagnostic Values of CEUS, CECT and CEMRI for Renal Cystic Lesions on the Current Bosniak Criterion-A Meta-analysi

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