ArticleEuropean radiology2019
Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it, 89 citations in OpenAlex.
- CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis.PloS one · 2023Pooled it
- Optimizing Deep Learning for Renal Mass Characterization in Challenging Cases: A Comparative Study of Spatial-Input Strategies and a Validated ROI-Only DLR Nomogram.Annals of surgical oncology · 2026Article
- A nomogram model for challenging cases: differentiating fat-poor angiomyolipoma from clear cell renal cell carcinoma in uncertain or misdiagnosed tumors.Abdominal radiology (New York) · 2026Article
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine.Nature reviews. Clinical oncology · 2025Review
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
- Automatic segmentation of clear cell renal cell carcinoma based on deep learning and a preliminary exploration of the tumor microenvironment.Translational andrology and urology · 2025Article
- Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma.Insights into imaging · 2024Article
- Radiogenomics and Texture Analysis to Detect von Hippel-Lindau (VHL) Mutation in Clear Cell Renal Cell Carcinoma.Current issues in molecular biology · 2024Review
- Machine learning-driven prognostic analysis of cuproptosis and disulfidptosis-related lncRNAs in clear cell renal cell carcinoma: a step towards precision oncology.European journal of medical research · 2024Article
- The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics.Cancers · 2024Review
- An automatic texture feature analysis framework of renal tumor: surgical, pathological, and molecular evaluation based on multi-phase abdominal CT.European radiology · 2024Article
- Validity of a multiphase CT-based radiomics model in predicting the Leibovich risk groups for localized clear cell renal cell carcinoma: an exploratory study.Insights into imaging · 2023Article
- Radiogenomics: a key component of precision cancer medicine.British journal of cancer · 2023Review
- Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects.International journal of molecular sciences · 2023Review
- Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma.European journal of radiology open · 2023Article
- Differentiation of Clear Cell and Non-clear-cell Renal Cell Carcinoma through CT-based Radiomics Models and Nomogram.Current medical imaging · 2023Article
- Automatic renal mass segmentation and classification on CT images based on 3D U-Net and ResNet algorithms.Frontiers in oncology · 2023Article
- Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends.Frontiers in oncology · 2023Review
- A primer on texture analysis in abdominal radiology.Abdominal radiology (New York) · 2022Review
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8 authors at 4 institutions in 1 country.
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Abstract
objectivesTo develop a radiomics model with all-relevant imaging features from multiphasic computed tomography (CT) for differentiating clear cell renal cell carcinoma (ccRCC) from non-ccRCC and to investigate the possible radiogenomics link between the imaging features and a key ccRCC driver gene-the von Hippel-Lindau (VHL) gene mutation.
methodsIn this retrospective two-center study, two radiomics models were built using random forest from a training cohort (170 patients), where one model was built with all-relevant features and the other with minimum redundancy maximum relevance (mRMR) features. A model combining all-relevant features and clinical factors (sex, age) was also built. The radiogenomics association between selected features and VHL mutation was investigated by Wilcoxon rank-sum test. All models were tested on an independent validation cohort (85 patients) with ROC curves analysis.
resultsThe model with eight all-relevant features from corticomedullary phase CT achieved an AUC of 0.949 and an accuracy of 92.9% in the validation cohort, which significantly outperformed the model with eight mRMR features (seven from nephrographic phase and one from corticomedullary phase) with an AUC of 0.851 and an accuracy of 81.2%. Combining age and sex did not benefit the performance. Five out of eight all-relevant features were significantly associated with VHL mutation, while all eight mRMR features were significantly associated with VHL mutation (false discovery rate-adjusted p < 0.05).
conclusionsAll-relevant features in corticomedullary phase CT can be used to differentiate ccRCC from non-ccRCC. Most subtype-discriminative imaging features were found to be significantly associated with VHL mutation, which may underlie the molecular basis of the radiomics features. KEY POINTS: • All-relevant features in corticomedullary phase CT can be used to differentiate ccRCC from non-ccRCC with high accuracy. • Most RCC-subtype-discriminative CT features were associated with the key RCC-driven gene-the VHL gene mutation. • Radiomics model can be more accurate and interpretable when the imaging features could reflect underlying molecular basis of RCC.
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