Your browser doesn't support javascript.
loading
The Efficacy of Pretreatment 18F-FDG PET-CT-Based Deep Learning Network Structure to Predict Survival in Nasopharyngeal Carcinoma.
Long, Zi-Chan; Ding, Xing-Chen; Zhang, Xian-Bin; Sun, Peng-Peng; Hao, Fu-Rong; Li, Zi-Rong; Hu, Man.
Afiliación
  • Long ZC; Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
  • Ding XC; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
  • Zhang XB; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
  • Shui-Yu; Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China.
  • Zheng-Fu; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
  • Sun PP; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
  • Hao FR; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
  • Li ZR; Department of Radiation Oncology, Weifang People's Hospital, Weifang, China.
  • Hu M; Manteia Technologies, Xiamen, China.
Clin Med Insights Oncol ; 17: 11795549231171793, 2023.
Article en En | MEDLINE | ID: mdl-37251551
Palabras clave
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Clin Med Insights Oncol Año: 2023 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Clin Med Insights Oncol Año: 2023 Tipo del documento: Article