Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics

The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi...

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Detalles Bibliográficos
Autores principales: Jiang, Yuming, Zhou, Kangneng, Sun, Zepang, Wang, Hongyu, Xie, Jingjing, Zhang, Taojun, Sang, Shengtian, Islam, Md Tauhidul, Wang, Jen-Yeu, Chen, Chuanli, Yuan, Qingyu, Xi, Sujuan, Li, Tuanjie, Xu, Yikai, Xiong, Wenjun, Wang, Wei, Li, Guoxin, Li, Ruijiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439253/
https://www.ncbi.nlm.nih.gov/pubmed/37557177
http://dx.doi.org/10.1016/j.xcrm.2023.101146

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439253/
https://www.ncbi.nlm.nih.gov/pubmed/37557177
http://dx.doi.org/10.1016/j.xcrm.2023.101146

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