Prognostic prediction based on histopathologic features of tumor microenvironment in colorectal cancer

PURPOSE: To automatically quantify colorectal tumor microenvironment (TME) in hematoxylin and eosin stained whole slide images (WSIs), and to develop a TME signature for prognostic prediction in colorectal cancer (CRC). METHODS: A deep learning model based on VGG19 architecture and transfer learning...

Descripción completa

Detalles Bibliográficos
Autores principales: Shi, Liang, Zhang, Yuhao, Wang, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10117979/
https://www.ncbi.nlm.nih.gov/pubmed/37089601
http://dx.doi.org/10.3389/fmed.2023.1154077

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10117979/
https://www.ncbi.nlm.nih.gov/pubmed/37089601
http://dx.doi.org/10.3389/fmed.2023.1154077

Ejemplares similares