Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response

Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response

Abstract Tertiary lymphoid structures (TLSs) have been associated with favorable immunotherapy responses and prognosis in various cancers. Despite their significance, their quantification using multiplex immunohistochemistry (mIHC) staining of T and B lymphocytes remains labor-intensive, limiting it...

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Dades bibliogràfiques
Autors principals: Ziqiang Chen, Xiaobing Wang, Zelin Jin, Bosen Li, Dongxian Jiang, Yanqiu Wang, Mengping Jiang, Dandan Zhang, Pei Yuan, Yahui Zhao, Feiyue Feng, Yicheng Lin, Liping Jiang, Chenxi Wang, Weida Meng, Wenjing Ye, Jie Wang, Wenqing Qiu, Houbao Liu, Dan Huang, Yingyong Hou, Xuefei Wang, Yuchen Jiao, Jianming Ying, Zhihua Liu, Yun Liu
Format: Article
Idioma:English
Publicat: Nature Portfolio 2024-03-01
Col·lecció:npj Precision Oncology
Accés en línia:https://doi.org/10.1038/s41698-024-00579-w

Internet

https://doi.org/10.1038/s41698-024-00579-w

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