Current status and future developments in predicting outcomes in radiation oncology.

Niraula, Dipesh; Cui, Sunan; Pakela, Julia; Wei, Lise; Luo, Yi; Ten Haken, Randall K; El Naqa, Issam

Niraula, Dipesh; Cui, Sunan; Pakela, Julia; Wei, Lise; Luo, Yi; Ten Haken, Randall K; El Naqa, Issam.

Affiliation

Niraula D; Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA.
Cui S; Department of Radiation Oncology, Stanford Medicine, Stanford University, Stanford, USA.
Pakela J; Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
Wei L; Department of Radiation Oncology, University of Michigan, Ann Arbor, USA.
Luo Y; Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA.
Ten Haken RK; Department of Radiation Oncology, University of Michigan, Ann Arbor, USA.
El Naqa I; Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA.

Br J Radiol ; 95(1139): 20220239, 2022 Oct 01.

Article de En | MEDLINE | ID: mdl-35867841

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Radio-oncologie Type d'étude: Prognostic_studies / Risk_factors_studies Limites: Humans Langue: En Journal: Br J Radiol Année: 2022 Type de document: Article Pays d'affiliation: États-Unis d'Amérique

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