Machine learning for normal tissue complication probability prediction: Predictive power with versatility and easy implementation.

Samant, Pratik; Ruysscher, Dirk de; Hoebers, Frank; Canters, Richard; Hall, Emma; Nutting, Chris; Maughan, Tim; Van den Heuvel, Frank

Samant, Pratik; Ruysscher, Dirk de; Hoebers, Frank; Canters, Richard; Hall, Emma; Nutting, Chris; Maughan, Tim; Van den Heuvel, Frank.

Afiliación

Samant P; Oxford University Hospitals NHS Foundation Trust, Radiotherapy Physics, Oxford, United Kingdom.
Ruysscher D; University of Oxford, Department of Oncology, Oxford, United Kingdom.
Hoebers F; Maastricht University Medical Centre, Department of Radiation Oncology (Maastro), Maastricht, The Netherlands.
Canters R; Maastricht University Medical Centre, Department of Radiation Oncology (Maastro), Maastricht, The Netherlands.
Hall E; Maastricht University Medical Centre, Department of Radiation Oncology (Maastro), Maastricht, The Netherlands.
Nutting C; Institute of Cancer Research, Division of Clinical Studies, Sutton, United Kingdom.
Maughan T; Institute of Cancer Research, Division of Radiotherapy and Imaging, Sutton, United Kingdom.
Van den Heuvel F; University of Oxford, Department of Oncology, Oxford, United Kingdom.

Clin Transl Radiat Oncol ; 39: 100595, 2023 Mar.

Article en En | MEDLINE | ID: mdl-36880063

Palabras clave

AB, AdaBooost (aka Adaptive Boosting); Clinical radiobiology; DA, Dual Annealing; DE, Differential Evolution; DT, Decision Tree; DVH, Dose Volume Histogram; GB, Gradient Boost; GD, Gradient Descent; GMD, Generalized Mean Dose; Head and Neck Cancer; LKB, Lyman Kutcher Burman; LR, Logistic Regression; ML, Machine Learning; Machine Learning; NTCP, Normal Tissue Complication Probability; Normal Tissue Complication Probability; OAR, Organ(s) at Risk; RT, Radiotherapy; Radiotherapy; Treatment Planning; Xerostomia

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Texto completo: 1 Colección: 01-internacional 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: Reino Unido Pais de publicación: Irlanda

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