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Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline.
Ye, Zezhong; Saraf, Anurag; Ravipati, Yashwanth; Hoebers, Frank; Zha, Yining; Zapaishchykova, Anna; Likitlersuang, Jirapat; Tishler, Roy B; Schoenfeld, Jonathan D; Margalit, Danielle N; Haddad, Robert I; Mak, Raymond H; Naser, Mohamed; Wahid, Kareem A; Sahlsten, Jaakko; Jaskari, Joel; Kaski, Kimmo; Mäkitie, Antti A; Fuller, Clifton D; Aerts, Hugo J W L; Kann, Benjamin H.
Afiliación
  • Ye Z; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States.
  • Saraf A; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
  • Ravipati Y; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States.
  • Hoebers F; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
  • Zha Y; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States.
  • Zapaishchykova A; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
  • Likitlersuang J; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States.
  • Tishler RB; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
  • Schoenfeld JD; Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands.
  • Margalit DN; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States.
  • Haddad RI; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
  • Mak RH; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States.
  • Naser M; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
  • Wahid KA; Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
  • Sahlsten J; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States.
  • Jaskari J; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
  • Kaski K; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
  • Mäkitie AA; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
  • Fuller CD; Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
  • Aerts HJWL; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
  • Kann BH; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States.
medRxiv ; 2023 Mar 06.
Article en En | MEDLINE | ID: mdl-36945519
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: MedRxiv Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: MedRxiv Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos