ArticleEuropean radiology2019
CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade.
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 41 papers, 2 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of CT texture analysis for differentiating low-grade and high-grade renal cell carcinoma: systematic review and meta-analysis.BMJ open · 2021Pooled it
- CT-based radiomics for differentiating renal tumours: a systematic review.Abdominal radiology (New York) · 2021Pooled it
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
- Data-driven image analysis to determine antibody-induced dissociation of cell-cell adhesion and antibody pathogenicity in pemphigus.PNAS nexus · 2025Article
- Integration of dual-source dual-energy CT quantitative parameters and ultrasound image features: A diagnostic method for extraglandular invasion of papillary thyroid carcinoma.Oncology letters · 2025Article
- Predicting Renal Cell Carcinoma Subtypes and Fuhrman Grading Using Multiphasic CT-Based Texture Analysis and Machine Learning Techniques.The Indian journal of radiology & imaging · 2025Article
- Deep learning using contrast-enhanced ultrasound images to predict the nuclear grade of clear cell renal cell carcinoma.World journal of urology · 2024Article
- CT radiomics to differentiate between Wilms tumor and clear cell sarcoma of the kidney in children.BMC medical imaging · 2024Article
- An automatic texture feature analysis framework of renal tumor: surgical, pathological, and molecular evaluation based on multi-phase abdominal CT.European radiology · 2024Article
- Tumor therapeutics in the era of "RECIST": past, current insights, and future prospects.Oncology reviews · 2024Review
- Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review.Cancers · 2023Review
- The clinical significance of computed tomography texture features of renal cell carcinoma in predicting pathological T1-3 staging.Quantitative imaging in medicine and surgery · 2023Article
- Texture analysis of diffusion kurtosis imaging for differentiating malignant from benign sinonasal lesions: added value to conventional imaging features.The British journal of radiology · 2023Article
- Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma.Frontiers in oncology · 2023Article
- Machine learning-based CT texture analysis in the differentiation of testicular masses.Frontiers in oncology · 2023Article
- Whole-Lesion CT Texture Analysis as a Quantitative Biomarker for the Identification of Homogeneous Renal Tumors.Life (Basel, Switzerland) · 2022Article
- Clinical-radiomic model in advanced kidney cancer predicts response to tyrosine kinase inhibitors.Oncology letters · 2022Article
- Review
- Contrast-Enhanced CT Protocol for the Fontan Pathway: Comparison Between 1- and 3-Minute Scan Delays.Pediatric cardiology · 2022Article
- Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review.European urology · 2022Review
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9 authors.
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Abstract
objectiveCT texture analysis (CTTA) using filtration-histogram-based parameters has been associated with tumor biologic correlates such as glucose metabolism, hypoxia, and tumor angiogenesis. We investigated the utility of these parameters for differentiation of clear cell from papillary renal cancers and prediction of Fuhrman grade.
methodsA retrospective study was performed by applying CTTA to pretreatment contrast-enhanced CT scans in 290 patients with 298 histopathologically confirmed renal cell cancers of clear cell and papillary types. The largest cross section of the tumor on portal venous phase axial CT was chosen to draw a region of interest. CTTA comprised of an initial filtration step to extract features of different sizes (fine, medium, coarse spatial scales) followed by texture quantification using histogram analysis.
resultsA significant increase in entropy with fine and medium spatial filters was demonstrated in clear cell RCC (p = 0.047 and 0.033, respectively). Area under the ROC curve of entropy at fine and medium spatial filters was 0.804 and 0.841, respectively. An increased entropy value at coarse filter correlated with high Fuhrman grade tumors (p = 0.01). The other texture parameters were not found to be useful.
conclusionEntropy, which is a quantitative measure of heterogeneity, is increased in clear cell renal cancers. High entropy is also associated with high-grade renal cancers. This parameter may be considered as a supplementary marker when determining aggressiveness of therapy. KEY POINTS: • CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis may help to separate different types of renal cancers. • CT texture analysis may enhance individualized treatment of renal cancers.
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