Comparing supervised and semi-supervised machine learning approaches in NTCP modeling to predict complications in head and neck cancer patients.

Spiero, I; Schuit, E; Wijers, O B; Hoebers, F J P; Langendijk, J A; Leeuwenberg, A M

Spiero, I; Schuit, E; Wijers, O B; Hoebers, F J P; Langendijk, J A; Leeuwenberg, A M.

Afiliación

Spiero I; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
Schuit E; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
Wijers OB; Radiotherapeutic Institute Friesland, Leeuwarden, the Netherlands.
Hoebers FJP; Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands.
Langendijk JA; Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Leeuwenberg AM; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.

Clin Transl Radiat Oncol ; 43: 100677, 2023 Nov.

Article en En | MEDLINE | ID: mdl-37822705

Palabras clave

Head and neck cancer; NTCP modeling; Radiation-induced toxicity; Semi-supervised learning

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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Clin Transl Radiat Oncol Año: 2023 Tipo del documento: Article País de afiliación: Países Bajos

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