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Artificial intelligence for response prediction and personalisation in radiation oncology.
Zwanenburg, Alex; Price, Gareth; Löck, Steffen.
Afiliação
  • Zwanenburg A; OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Fetscherstr. 74, PF 41, 01307, Dresden, Germany. alexander.zwanenburg@nct-dresden.de.
  • Price G; National Center for Tumor Diseases Dresden (NCT/UCC), Germany:, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germ
  • Löck S; German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany. alexander.zwanenburg@nct-dresden.de.
Strahlenther Onkol ; 2024 Aug 30.
Article em En | MEDLINE | ID: mdl-39212687
ABSTRACT
Artificial intelligence (AI) systems may personalise radiotherapy by assessing complex and multifaceted patient data and predicting tumour and normal tissue responses to radiotherapy. Here we describe three distinct generations of AI systems, namely personalised radiotherapy based on pretreatment data, response-driven radiotherapy and dynamically optimised radiotherapy. Finally, we discuss the main challenges in clinical translation of AI systems for radiotherapy personalisation.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article