Bosniak Classification version 2019: validation and comparison to original classification in pathologically confirmed cystic masses

Yan, Jin; Chan, Jason; Osman, Hussein Al; Munir, Javeria; Alrasheed, Sumaya; Flood, Trevor A.; Schieda, Nicola

doi:10.1007/s00330-021-08006-5

Cited by 32 publications

(24 citation statements)

References 11 publications

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“…The semantic annotations of CT images and the corresponding diagnostic criteria were as follows: (a) tumor size, defined as the maximum diameter on transverse images; (b) intratumoral necrosis, defined as the non-enhanced fluid region of the tumor, which was greater than 50% of the tumor [ 27 ]; (c) cystic degeneration, defined as target lesion showing uniform water density and signal intensity, but no enhancement on enhancement examination [ 28 ]; (d) intratumoral calcification, interpreted as obvious dense shadows in the parenchyma that were speckled, lined, or shell-shaped; (e) violation of the renal capsule, interpreted as abnormal lesion violating the margin of the renal capsule; (f) intratumoral angiogenesis, defined as vascular enhancement observed in the parenchyma of the cortical stage tumor [ 27 , 29 ]; (g) venous invasion, interpreted as radiological characteristics of tumor thrombosis in the renal vein and inferior vena cava [ 27 ]; (h) perinephric metastasis, defined as perinephric invasion phenomenon on CT images; and (i) distant metastasis, considered as metastasis in the lung, liver, bone, brain, or other organs via the blood or lymphatics. In our study, two radiologists with 10 or more years of experience in renal imaging who were blinded to histopathological results independently identified and evaluated these characteristics.…”

Section: Methodsmentioning

confidence: 99%

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Guo

et al. 2021

Insights Imaging

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Purpose To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). Methods This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. Results SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model. Conclusion A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.

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Section: Methodsmentioning

confidence: 99%

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Guo

et al. 2021

Insights Imaging

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“…The average tumor size was between 25.4 mm and 65.9 mm, with a malignancy rate of 18.8%–78.1%. Regarding the imaging modalities, four studies reported results from CT ( 21 , 23 , 25 , 28 ),, five reported results from MRI ( 22 , 23 , 25 , 26 , 28 ),, and three reported a combination of results from CT and MRI ( 21 , 24 , 27 ). With respect to the number of radiologists, only one study reported that the images were interpreted by only one reader ( 24 ), whereas in the other seven studies, the images were interpreted by at least two readers.…”

Section: Resultsmentioning

confidence: 99%

“…(5). Given that a head-to-head comparison was reported in five of the included studies, we used them to compare the two Bosniak classification versions (22,23,(25)(26)(27). According to our analyses, the Bosniak classification, version 2019 is significantly superior to version 2005 in terms of specificity (0.62 vs. 0.41, p < 0.001); however, it comes at the cost of a substantial decline in sensitivity (0.88 vs. 0.94, p = 0.001).…”

Section: Discussionmentioning

confidence: 99%

“…For the five studies reporting the diagnostic accuracy of MRI, the pooled sensitivity was comparable (0.87, 95% CI 0.78-0.93) but the specificity was lower (0.67, 95% CI 0.48-0.81) (22,23,25,26,28). Regarding the three studies reporting the diagnostic accuracy of CT in combination with MRI, the pooled sensitivity (0.81, 95% CI 0.70-0.91) was lower than using CT or MRI alone, while the specificity was comparable (0.67, 95% CI 0.61-0.82) (21,24,27).…”

Section: Subgroup Analysesmentioning

confidence: 97%

“…The overall quality of the included studies was not very high, primarily due to patient selection (Figure 2). In more than half of the studies, the analysis was restricted to cystic renal masses with confirmed pathological results (23,(25)(26)(27)(28), which may have led to selection and verification biases, as those masses under surveillance that did not undergo histological confirmation were not included. Regarding the reference standard, two of the studies used the availability of at least fi ve years of follow-up data, rather than histopathological findings, as the reference standard for some patients (21,22).…”

Section: Quality Assessmentmentioning

confidence: 99%

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

Lan,

Yang,

et al. 2023

J of Ultrasound Medicine

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ObjectivesThe 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.MethodsA 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.ResultsA 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.ConclusionsBosniak 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.

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Bosniak Classification version 2019: validation and comparison to original classification in pathologically confirmed cystic masses

Cited by 32 publications

References 11 publications

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

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

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

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