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Deep Learning Features Improve the Performance of a Radiomics Signature for Predicting KRAS Status in Patients with Colorectal Cancer.
Wu, Xiaomei; Li, Yajun; Chen, Xin; Huang, Yanqi; He, Lan; Zhao, Ke; Huang, Xiaomei; Zhang, Wen; Huang, Yucun; Li, Yexing; Dong, Mengyi; Huang, Jia; Xia, Ting; Liang, Changhong; Liu, Zaiyi.
  • Wu X; School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, China; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, Guangdong Province, China.
  • Li Y; School of Computer Science Engineering, South China University of Technology, Guangzhou, Guangdong Province, China.
  • Chen X; Department of Radiology, Guangzhou First People's Hospital, Guangzhou, Guangdong Province, China.
  • Huang Y; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, Guangdong Province, China.
  • He L; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, Guangdong Province, China.
  • Zhao K; School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, China; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, Guangdong Province, China.
  • Huang X; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, Guangdong Province, China; Southern Medical University, Guangzhou, Guangdong Province, PR China.
  • Zhang W; Southern Medical University, Guangzhou, Guangdong Province, PR China.
  • Huang Y; Southern Medical University, Guangzhou, Guangdong Province, PR China.
  • Li Y; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, Guangdong Province, China; Shantou University, Shantou, Guangdong Province, PR China.
  • Dong M; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, Guangdong Province, China; Southern Medical University, Guangzhou, Guangdong Province, PR China.
  • Huang J; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, Guangdong Province, China; Shantou University, Shantou, Guangdong Province, PR China.
  • Xia T; School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, China; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, Guangdong Province, China.
  • Liang C; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, Guangdong Province, China.
  • Liu Z; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, Guangdong Province, China. Electronic address: zyliu@163.com.
Acad Radiol ; 27(11): e254-e262, 2020 11.
Article en En | MEDLINE | ID: mdl-31982342
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias Colorrectales / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2020 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Search on Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias Colorrectales / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2020 Tipo del documento: Article