Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images
- 1 February 2020
- journal article
- research article
- Published by Elsevier in Academic Radiology
- Vol. 27 (2) , 157-168
Abstract
No abstract availableKeywords
Funding Information
- National Institutes of Health (81801671)
- Science and Technology Bureau of Luzhou China (12126)
This publication has 31 references indexed in Scilit:
- Angiomyolipoma with Minimal FatAcademic Radiology, 2015
- 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
- Effective texture classification by texton encoding induced statistical featuresPattern Recognition, 2015
- Textural Differences in Apparent Diffusion Coefficient Between Low- and High-Stage Clear Cell Renal Cell CarcinomaAmerican Journal of Roentgenology, 2014
- Clinical behavior of chromophobe renal cell carcinoma is less aggressive than that of clear cell renal cell carcinoma, independent of Fuhrman grade or tumor sizeVirchows Archiv, 2014
- Large Sample Size, Wide Variant Spectrum, and Advanced Machine-Learning Technique Boost Risk Prediction for Inflammatory Bowel DiseaseThe American Journal of Human Genetics, 2013
- MaZda—A software package for image texture analysisComputer Methods and Programs in Biomedicine, 2009
- Solid Renal Tumors: An Analysis of Pathological Features Related to Tumor SizeJournal of Urology, 2003
- Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive modelThe Lancet, 2003