A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma

Radiomics: Images Are More than Pictures, They Are Data

Robert J. Gillies, Paul E. Kinahan, Hedvig Hricak · Radiology · 2015 · 8.3K citations

Medicine, Bioinformatics, Biomedical Informatics +17

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

Gary S. Collins, Johannes B. Reitsma, Douglas G. Altman et al. · BMJ · 2015 · 4K citations · Full text

Computing, Internal Medicine, Clinical Decision Support +16

Assessment of tumor heterogeneity by CT texture analysis: Can the largest cross-sectional area be used as an alternative to whole tumor analysis?

Francesca Ng, Robert Kozarski, Balaji Ganeshan et al. · European Journal of Radiology · 2012 · 364 citations · Full text

Medicine, Oncology, Radiology +17

Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

Taryn Hodgdon, Matthew D. F. McInnes, Nicola Schieda et al. · Radiology · 2015 · 257 citations

Medicine, Pathology, Radiology +17

Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma

Zhichao Feng, Pengfei Rong, Peng Cao et al. · European Radiology · 2017 · 224 citations

Medicine, Computer Science, Radiology +17