Clinical use of machine learning‐based pathomics signature for diagnosis and survival prediction of bladder cancer

Traditional histopathology performed by pathologists by the naked eye is insufficient for accurate and efficient diagnosis of bladder cancer (BCa). We collected 643 H&E‐stained BCa images from Shanghai General Hospital and The Cancer Genome Atlas (TCGA). We constructed and cross‐verified automat...

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Detalles Bibliográficos
Autores principales: Chen, Siteng, Jiang, Liren, Zheng, Xinyi, Shao, Jialiang, Wang, Tao, Zhang, Encheng, Gao, Feng, Wang, Xiang, Zheng, Junhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253293/
https://www.ncbi.nlm.nih.gov/pubmed/33931925
http://dx.doi.org/10.1111/cas.14927

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253293/
https://www.ncbi.nlm.nih.gov/pubmed/33931925
http://dx.doi.org/10.1111/cas.14927

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