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[Analysis of the Tumor Immune Microenvironment of Colorectal Cancer by Deep Learning-Based Imaging Cytometry].
Yamashita, Kimihiro; Nagasaka, Toru; Adachi, Yukari; Abe, Tomoki; Kakeji, Yoshihiro.
Afiliação
  • Yamashita K; Dept. of Surgery, Division of Gastrointestinal Surgery, Kobe University Graduate School of Medicine.
Gan To Kagaku Ryoho ; 50(9): 955-957, 2023 Sep.
Article em Ja | MEDLINE | ID: mdl-37800286
The tumor immune microenvironment(TIME)of colorectal cancer contains indicators of unique therapeutic outcomes for each cancer patient. Deep learning-based imaging cytometry(DL-IC), which can obtain objective and reproducible cell- related information in tissue sections, has attracted attention as an analytical method for clarifying this indicator. This study demonstrates the validation process of Cu-Cyto, one of DL-IC, regarding cell identification accuracy. Acquisition of"spatial structure"information in TIME is useful for biomarker retrieval and contributes to precision oncology.
Assuntos
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Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans Idioma: Ja Revista: Gan To Kagaku Ryoho Ano de publicação: 2023 Tipo de documento: Article
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Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans Idioma: Ja Revista: Gan To Kagaku Ryoho Ano de publicação: 2023 Tipo de documento: Article