ArticleCancer imaging : the official publication of the International Cancer Imaging Society2019
CT texture analysis: a potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline 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
- Radiomics-based tumor heterogeneity augments clinicopathological models for predicting recurrence in high-risk clear cell renal cell carcinoma after nephrectomy.Abdominal radiology (New York) · 2026Article
- Radiomic model versus R.E.N.A.L. nephrometry score for predicting the likelihood of undergoing partial nephrectomy in clear cell renal cell carcinoma.Frontiers in urology · 2026Article
- CT-Based Texture Analysis in Indeterminate Pediatric Renal and Pararenal Masses.The Indian journal of radiology & imaging · 2026Article
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
- Hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) showing iso- or hyperintensity in the hepatobiliary phase: differentiation using Gd-EOB-DTPA enhanced MRI radiomics and deep learning features.BMC medical imaging · 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
- Grading of clear cell renal cell carcinoma using diffusion MRI with a multimodal apparent diffusion model.Frontiers in oncology · 2025Article
- CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma.The British journal of radiology · 2024Article
- Multiphase comparative study for WHO/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma.Scientific reports · 2024Article
- Comparative evaluation of multiparametric lumbar MRI radiomic models for detecting osteoporosis.BMC musculoskeletal disorders · 2024Article
- Computed tomography texture-based models for predicting KIT exon 11 mutation of gastrointestinal stromal tumors.Heliyon · 2023Article
- Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review.Cancers · 2023Review
- Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma.Frontiers in oncology · 2023Article
- Whole-Lesion CT Texture Analysis as a Quantitative Biomarker for the Identification of Homogeneous Renal Tumors.Life (Basel, Switzerland) · 2022Article
- An exploratory study of CT radiomics using differential network feature selection for WHO/ISUP grading and progression-free survival prediction of clear cell renal cell carcinoma.Frontiers in oncology · 2022Article
- Prediction Models for Prognosis of Femoral Neck-Fracture Patients 6 Months after Total Hip Arthroplasty.International journal of general medicine · 2022Article
- Response to Induction Therapy in Pediatric Hodgkin's Lymphoma: Performance of First-Order Texture Parameters of CT Images.Journal of the Belgian Society of Radiology · 2022Article
- CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma.Frontiers in oncology · 2022Article
- Tumor grade estımatıon of clear cell and papıllary renal cell carcınomas usıng contrast-enhanced MDCT and FSE T2 weıghted MR ımagıng: radıology-pathology correlatıon.La Radiologia medica · 2021Article
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4 authors.
Funding
Abstract
backgroundThe purpose of this study was to analyze the image heterogeneity of clear-cell renal-cell carcinoma (ccRCC) by computer tomography texture analysis and to provide new objective quantitative imaging parameters for the pre-operative prediction of Fuhrman-grade ccRCC.
methodsA retrospective analysis of 131 cases of ccRCCs was performed by manually depicting tumor areas. Then, histogram-based texture parameters were calculated. The texture-feature values between Fuhrman low- (Grade I-II) and high-grade (Grade III-IV) ccRCCs were compared by two independent sample t-tests (False Discovery Rate correction), and receiver operating characteristic curve (ROC) was used to evaluate the efficacy of using texture features to predict Fuhrman high- and low-grade ccRCCs.
resultsThere were no statistical differences for any texture parameters without filtering (p > 0.05). There was a statistically significant difference between the entropy (fine) of the corticomedullary phase and the entropy (fine and coarse) of the nephrographic phase after Laplace of Gaussian filtering. The area under the ROC of the entropy was between 0.74 and 0.83.
conclusionsComputer tomography texture features can predict the Fuhrman grading of ccRCC pre-operatively, with entropy being the most important imaging marker for clinical application.
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