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CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma.
Ma, Baoqiang; Guo, Jiapan; Zhai, Tian-Tian; van der Schaaf, Arjen; Steenbakkers, Roel J H M; van Dijk, Lisanne V; Both, Stefan; Langendijk, Johannes A; Zhang, Weichuan; Qiu, Bingjiang; van Ooijen, Peter M A; Sijtsema, Nanna M.
Afiliação
  • Ma B; Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands.
  • Guo J; Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands.
  • Zhai TT; Machine Learning Lab, Data Science Centre in Health (DASH), University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands.
  • van der Schaaf A; Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, the Netherlands.
  • Steenbakkers RJHM; Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
  • van Dijk LV; Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands.
  • Both S; Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands.
  • Langendijk JA; Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands.
  • Zhang W; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Centre, Houston, Texas, USA.
  • Qiu B; Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands.
  • van Ooijen PMA; Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands.
  • Sijtsema NM; Institute for Integrated and Intelligent Systems, Griffith University, Queensland, Australia.
Med Phys ; 50(10): 6190-6200, 2023 Oct.
Article em En | MEDLINE | ID: mdl-37219816
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Carcinoma de Células Escamosas / Neoplasias Orofaríngeas / Neoplasias de Cabeça e Pescoço Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Carcinoma de Células Escamosas / Neoplasias Orofaríngeas / Neoplasias de Cabeça e Pescoço Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article