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Prognostic Analysis Combining Histopathological Features and Clinical Information to Predict Colorectal Cancer Survival from Whole-Slide Images.

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Title: Prognostic Analysis Combining Histopathological Features and Clinical Information to Predict Colorectal Cancer Survival from Whole-Slide Images.
Authors: Cai C; School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China. chengfeicai@nuist.edu.cn.; College of Information Engineering, Taizhou University, Taizhou, 225300, China. chengfeicai@nuist.edu.cn.; Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China. chengfeicai@nuist.edu.cn., Zhou Y; Department of Pathology, Zhujiang Hospital of Southern Medical University, Guangzhou, 510280, China., Jiao Y; Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China., Li L; Department of Pathology, Nanfang Hospital of Southern Medical University, Guangzhou, 510515, China., Xu J; Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Source: Digestive diseases and sciences [Dig Dis Sci] 2024 Aug; Vol. 69 (8), pp. 2985-2995. Date of Electronic Publication: 2024 Jun 05.
Publication Type: Journal Article
Journal Info: Publisher: Springer Science + Business Media Country of Publication: United States NLM ID: 7902782 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-2568 (Electronic) Linking ISSN: 01632116 NLM ISO Abbreviation: Dig Dis Sci Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Prognostic Analysis Combining Histopathological Features and Clinical Information to Predict Colorectal Cancer Survival from Whole-Slide Images.
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  Data: <searchLink fieldCode="AU" term="%22Cai+C%22">Cai C</searchLink>; School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China. chengfeicai@nuist.edu.cn.; College of Information Engineering, Taizhou University, Taizhou, 225300, China. chengfeicai@nuist.edu.cn.; Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China. chengfeicai@nuist.edu.cn.<br /><searchLink fieldCode="AU" term="%22Zhou+Y%22">Zhou Y</searchLink>; Department of Pathology, Zhujiang Hospital of Southern Medical University, Guangzhou, 510280, China.<br /><searchLink fieldCode="AU" term="%22Jiao+Y%22">Jiao Y</searchLink>; Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China.<br /><searchLink fieldCode="AU" term="%22Li+L%22">Li L</searchLink>; Department of Pathology, Nanfang Hospital of Southern Medical University, Guangzhou, 510515, China.<br /><searchLink fieldCode="AU" term="%22Xu+J%22">Xu J</searchLink>; Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
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  Data: <searchLink fieldCode="JN" term="%227902782%22">Digestive diseases and sciences</searchLink> [Dig Dis Sci] 2024 Aug; Vol. 69 (8), pp. 2985-2995. <i>Date of Electronic Publication: </i>2024 Jun 05.
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        Value: 10.1007/s10620-024-08501-x
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      – TitleFull: Prognostic Analysis Combining Histopathological Features and Clinical Information to Predict Colorectal Cancer Survival from Whole-Slide Images.
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            NameFull: Zhou Y
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              Text: 2024 Aug
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