ArticleMolecular imaging
Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma.
Article in Molecular imaging. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The influence of manual segmentation strategies and different phases selection on machine learning-based computed tomography in renal tumors: a systematic review and meta-analysis.La Radiologia medica · 2024Pooled it
- CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis.Clinical imaging · 2023Pooled it
- CT-based radiomics for differentiating renal tumours: a systematic review.Abdominal radiology (New York) · 2021Pooled it
- Multiphase CT-derived markers for the characterization of large solid and cystic renal masses by histology and grade.Polish journal of radiology · 2026Article
- Combined Clear Cell Renal Cell Carcinoma and Chromophobe Renal Cell Carcinoma: A Case Report.Cureus · 2024Article
- Radiomics analysis based on single phase and different phase combinations of radiomics features from tri-phasic CT to distinguish renal oncocytoma from chromophobe renal cell carcinoma.Abdominal radiology (New York) · 2024Article
- Whole-Lesion CT Texture Analysis as a Quantitative Biomarker for the Identification of Homogeneous Renal Tumors.Life (Basel, Switzerland) · 2022Article
- A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images.European radiology · 2022Article
- Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase-enhanced CT images.European radiology · 2021Article
- Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma.Tomography (Ann Arbor, Mich.) · 2020Review
- MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma.The British journal of radiology · 2020Article
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10 authors.
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Abstract
objectiveTo evaluate the value of 2-dimensional (2D) and 3-dimensional (3D) computed tomography texture analysis (CTTA) models in distinguishing fat-poor angiomyolipoma (fpAML) from chromophobe renal cell carcinoma (chRCC).
methodsWe retrospectively enrolled 32 fpAMLs and 24 chRCCs. Texture features were extracted from 2D and 3D regions of interest in triphasic CT images. The 2D and 3D CTTA models were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The diagnostic performance of the 2D and 3D CTTA models was evaluated with respect to calibration, discrimination, and clinical usefulness.
resultsOf the 177 and 183 texture features extracted from 2D and 3D regions of interest, respectively, 5 2D features and 8 3D features were selected to build 2D and 3D CTTA models. The 2D CTTA model (area under the curve [AUC], 0.811; 95% confidence interval [CI], 0.695-0.927) and the 3D CTTA model (AUC, 0.915; 95% CI, 0.838-0.993) showed good discrimination and calibration (
conclusionsThe CTTA models based on contrast-enhanced CT images had a high value in differentiating fpAML from chRCC.
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