Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade
Authors:
- 1 December 2019
- journal article
- research article
- Published by Elsevier in European Journal of Radiology
- Vol. 121, 108738
Abstract
No abstract availableKeywords
Funding Information
- Discipline promotion project of Xijing hospital (XJZT18ML76)
This publication has 28 references indexed in Scilit:
- Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?Radiology, 2015
- Diagnosis of Sarcomatoid Renal Cell Carcinoma With CT: Evaluation by Qualitative Imaging Features and Texture AnalysisAmerican Journal of Roentgenology, 2015
- The International Society of Urological Pathology (ISUP) Grading System for Renal Cell Carcinoma and Other Prognostic ParametersAmerican Journal of Surgical Pathology, 2013
- Assessment of tumor heterogeneity by CT texture analysis: Can the largest cross-sectional area be used as an alternative to whole tumor analysis?European Journal of Radiology, 2013
- Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survivalEuropean Radiology, 2011
- Grading of Clear Cell Renal Cell Carcinoma Should be Based on Nucleolar ProminenceAmerican Journal of Surgical Pathology, 2011
- Active Surveillance of Small Renal Masses: Progression Patterns of Early Stage Kidney CancerEuropean Urology, 2011
- Advances and controversies in grading and staging of renal cell carcinomaLaboratory Investigation, 2009
- Prognostic significance of morphologic parameters in renal cell carcinomaAmerican Journal of Surgical Pathology, 1982
- Texture analysis using gray level run lengthsComputer Graphics and Image Processing, 1975