ArticleEuropean radiology2022
Malignancy risk stratification of cystic renal lesions based on a contrast-enhanced CT-based machine learning model and a clinical decision algorithm.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of Machine Learning Models Based on Medical Imaging in Predicting Pathological Grade of Clear Cell Renal Cell Carcinoma.Cancer medicine · 2026Pooled it
- Machine learning pipeline for automated segmentation and classification of complicated cystic renal masses on MRI: comparison with radiologists' assessments.Japanese journal of radiology · 2026Article
- Deep learning with interactive segmentation for risk stratification of cystic renal lesions on tri-phase CT: a multicenter study.Frontiers in oncology · 2026Article
- Cystic renal mass screening: machine-learning-based radiomics on unenhanced computed tomographyDiagnostic and interventional radiology (Ankara, Turkey) · 2024Article
- A contrast-enhanced computed tomography-based radiomics nomogram for preoperative differentiation between benign and malignant cystic renal lesions.Translational andrology and urology · 2024Article
- Long-Term Oncologic Outcomes of Off-Clamp Robotic Partial Nephrectomy for Cystic Renal Tumors: A Propensity Score Matched-Pair Comparison of Cystic versus Pure Clear Cell Carcinoma.Current oncology (Toronto, Ont.) · 2024Article
- Deep learning system for malignancy risk prediction in cystic renal lesions: a multicenter study.Insights into imaging · 2024Article
- Pilot Study for the Assessment of the Best Radiomic Features for Bosniak Cyst Classification Using Phantom and Radiologist Inter-Observer Selection.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions.Insights into imaging · 2023Article
- Article
- Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.Frontiers in medicine · 2022Article
- Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm.Frontiers in oncology · 2022Article
- Artificial Intellgence in the Era of Precision Oncological Imaging.Technology in cancer research & treatmentReview
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9 authors.
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Abstract
objectiveTo distinguish benign from malignant cystic renal lesions (CRL) using a contrast-enhanced CT-based radiomics model and a clinical decision algorithm.
methodsThis dual-center retrospective study included patients over 18 years old with CRL between 2005 and 2018. The reference standard was histopathology or 4-year imaging follow-up. Training and testing datasets were acquired from two institutions. Quantitative 3D radiomics analyses were performed on nephrographic phase CT images. Ten-fold cross-validated LASSO regression was applied to the training dataset to identify the most discriminative features. A logistic regression model was trained to classify malignancy and tested on the independent dataset. Reported metrics included areas under the receiver operating characteristic curves (AUC) and balanced accuracy. Decision curve analysis for stratifying patients for surgery was performed in the testing dataset. A decision algorithm was built by combining consensus radiological readings of Bosniak categories and radiomics-based risks.
resultsA total of 149 CRL (139 patients; 65 years [56-72]) were included in the training dataset-35 Bosniak(B)-IIF (8.6% malignancy), 23 B-III (43.5%), and 23 B-IV (87.0%)-and 50 CRL (46 patients; 61 years [51-68]) in the testing dataset-12 B-IIF (8.3%), 10 B-III (60.0%), and 9 B-IV (100%). The machine learning model achieved high diagnostic performance in predicting malignancy in the testing dataset (AUC = 0.96; balanced accuracy = 94%). There was a net benefit across threshold probabilities in using the clinical decision algorithm over management guidelines based on Bosniak categories.
conclusionCT-based radiomics modeling accurately distinguished benign from malignant CRL, outperforming the Bosniak classification. The decision algorithm best stratified lesions for surgery and active surveillance. KEY POINTS: • The radiomics model achieved excellent diagnostic performance in identifying malignant cystic renal lesions in an independent testing dataset (AUC = 0.96). • The machine learning-enhanced decision algorithm outperformed the management guidelines based on the Bosniak classification for stratifying patients to surgical ablation or active surveillance.
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