Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study

PURPOSE: There are undetectable levels of fat in fat-poor angiomyolipoma. Thus, it is often misdiagnosed as renal cell carcinoma. We aimed to develop and evaluate a multichannel deep learning model for differentiating fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma (RCC). METHODS: This tw...

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Detalles Bibliográficos
Autores principales: Yao, Haohua, Tian, Li, Liu, Xi, Li, Shurong, Chen, Yuhang, Cao, Jiazheng, Zhang, Zhiling, Chen, Zhenhua, Feng, Zihao, Xu, Quanhui, Zhu, Jiangquan, Wang, Yinghan, Guo, Yan, Chen, Wei, Li, Caixia, Li, Peixing, Wang, Huanjun, Luo, Junhang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10620299/
https://www.ncbi.nlm.nih.gov/pubmed/37672075
http://dx.doi.org/10.1007/s00432-023-05339-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10620299/
https://www.ncbi.nlm.nih.gov/pubmed/37672075
http://dx.doi.org/10.1007/s00432-023-05339-0

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