Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning.

Huang, Kaimei; Lin, Binghu; Liu, Jinyang; Liu, Yankun; Li, Jingwu; Tian, Geng; Yang, Jialiang

Huang, Kaimei; Lin, Binghu; Liu, Jinyang; Liu, Yankun; Li, Jingwu; Tian, Geng; Yang, Jialiang.

Afiliación

Huang K; Department of Mathematics, Zhejiang Normal University, Jinghua 321004, China.
Lin B; Department of Sciences, Geneis (Beijing) Co., Ltd, Beijing 100102, China.
Liu J; Department of Sciences, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China.
Liu Y; Department of General Surgery of Third Ward, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China.
Li J; Department of Sciences, Geneis (Beijing) Co., Ltd, Beijing 100102, China.
Tian G; Department of Sciences, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China.
Yang J; Cancer Institute, Tangshan People's Hospital, Tangshan 063001, China.

Bioinformatics ; 38(22): 5108-5115, 2022 11 15.

Article en En | MEDLINE | ID: mdl-36130268

Asunto(s)

Neoplasias Colorrectales; Aprendizaje Profundo; Humanos; Mutación; Biomarcadores de Tumor/genética; Inmunoterapia/métodos; Neoplasias Colorrectales/genética

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Colorrectales / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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