Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas
Abstract This study was to assess the effect of the predictive model for distinguishing clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC) by establishing predictive radiomic models based on enhanced-computed tomography (CT) images of renal cell carcinoma (RCC). A total of 190 cases with RCC...
Guardado en:
Autores principales: | Ping Wang, Xu Pei, Xiao-Ping Yin, Jia-Liang Ren, Yun Wang, Lu-Yao Ma, Xiao-Guang Du, Bu-Lang Gao |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/acf1a865aae440a3b0aee58e4b605312 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
The Potential of Visceral Adipose Tissue in Distinguishing Clear Cell Renal Cell Carcinoma from Renal Angiomyolipoma with Minimal Fat
por: Liu J, et al.
Publicado: (2021) -
Immune classification of clear cell renal cell carcinoma
por: Sumeyye Su, et al.
Publicado: (2021) -
A convention-radiomics CT nomogram for differentiating fat-poor angiomyolipoma from clear cell renal cell carcinoma
por: Yanqing Ma, et al.
Publicado: (2021) -
Clonal architectures predict clinical outcome in clear cell renal cell carcinoma
por: Yi Huang, et al.
Publicado: (2019) -
Siglec-15 promotes progression of clear renal cell carcinoma
por: Wen-Bo Yang, et al.
Publicado: (2021)