Artificial intelligence for response prediction and personalisation in radiation oncology

Alex Zwanenburg; Gareth Price; Steffen Löck

doi:10.1007/s00066-024-02281-z

Artificial intelligence for response prediction and personalisation in radiation oncology

Strahlenther Onkol. 2024 Aug 30. doi: 10.1007/s00066-024-02281-z. Online ahead of print.

Authors

Alex Zwanenburg^{1

2

3}, Gareth Price^{4

5}, Steffen Löck^{6

7}

Affiliations

¹ 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. [email protected].
² 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, Germany. [email protected].
³ German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany. [email protected].
⁴ Division of Cancer Sciences, University of Manchester, Manchester, UK.
⁵ The Christie NHS Foundation Trust, Manchester, UK.
⁶ 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.
⁷ Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.

PMID: 39212687
DOI: 10.1007/s00066-024-02281-z

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.

Keywords: Artificial intelligence; Normal tissue complication probability; Radiotherapy; Treatment response; Tumour control probability.

Publication types

Review