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

Dana, Jérémy; Gauvin, Simon; Zhang, Michelle; Lotero, Jose; Cassim, Christopher; Artho, Giovanni; Bhatnagar, Sahir; Tanguay, Simon; Reinhold, Caroline

doi:10.1007/s00330-022-09082-x

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…In the 2019 version of the Bosniak classification of cystic renal masses, additional quantitative and discriminative criteria were introduced to further enhance the specificity in predicting the likelihood of malignancy in CRLs ( 4 ). On the other hand, no significant improvement in diagnostic performance and interreader agreement is shown between v2005 and v2019 ( 5 ). Interobserver agreement remains to be solved mainly in Classes II, IIF (in which F represents follow-up) and III.…”

Section: Introductionmentioning

confidence: 91%

A contrast-enhanced computed tomography-based radiomics nomogram for preoperative differentiation between benign and malignant cystic renal lesions

Yu,

Yan,

et al. 2024

Transl Androl Urol

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Section: Introductionmentioning

confidence: 91%

A contrast-enhanced computed tomography-based radiomics nomogram for preoperative differentiation between benign and malignant cystic renal lesions

Yu,

Yan,

et al. 2024

Transl Androl Urol

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“…Inaccurate treatment and associated diagnostic errors caused by the misapplication of Bosniak categorization may lead to excessive medical care following adverse results like renal function impairment, re-operation surgery and medical disputes [ 5 , 6 ]. It has been demonstrated that the diagnostic performance of 2019-Bosniak classification criteria do not significantly improve over its previous version [ 7 – 9 ]. According to the 2019-Bosniak version, a considerable proportion of previously diagnosed Class III lesions will be reclassified as IIF, resulting in lower sensitivity [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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

Feng

et al. 2023

Insights Imaging

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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.

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“…Based on the updated Bosniak classification, one or more enhancing nodules in CRL with obtuse margins (more than 4 mm) or with acute margins represent malignant renal neoplasms. Thickened wall or septa with enhancement in CRL also suggests the possibility of malignancy ( 4 ). However, according to the Bosniak classification, these high-risk CRL (IIF, III, and IV) could still be benign renal cysts rather than malignant neoplasms.…”

Section: Introductionmentioning

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

Cited by 7 publications

References 21 publications

A contrast-enhanced computed tomography-based radiomics nomogram for preoperative differentiation between benign and malignant cystic renal lesions

A contrast-enhanced computed tomography-based radiomics nomogram for preoperative differentiation between benign and malignant cystic renal lesions

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

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

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