Machine learning applications in radiation oncology: Current use and needs to support clinical implementation

Charlotte L Brouwer*, Anna M Dinkla, Liesbeth Vandewinckele, Wouter Crijns, Michaël Claessens, Dirk Verellen, Wouter van Elmpt

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Background and purpose: The use of artificial intelligence (AI)/ machine learning (ML) applications in radiation oncology is emerging, however no clear guidelines on commissioning of ML-based applications exist. The purpose of this study was therefore to investigate the current use and needs to support implementation of ML-based applications in routine clinical practice.

Materials and methods: A survey was conducted among medical physicists in radiation oncology, consisting of four parts: clinical applications (1), model training, acceptance and commissioning (2), quality assurance (QA) in clinical practice and General Data Protection Regulation (GDPR) (3), and need for education and guidelines (4). Survey answers of medical physicists of the same radiation oncology centre were treated as a separate unique responder in case reporting on different AI applications.

Results: In total, 213 medical physicists from 202 radiation oncology centres were included in the analysis. Sixty-nine percent (1 4 7) was using (37%) or preparing (32%) to use ML in clinic, mostly for contouring and treatment planning. In 86%, human observers were still involved in daily clinical use for quality check of the output of the ML algorithm. Knowledge on ethics, legislation and data sharing was limited and scattered among responders. Besides the need for (implementation) guidelines, training of medical physicists and larger databases containing multicentre data was found to be the top priority to accommodate the further introduction of ML in clinical practice.

Conclusion: The results of this survey indicated the need for education and guidelines on the implementation and quality assurance of ML-based applications to benefit clinical introduction.

Original languageEnglish
Pages (from-to)144-148
Number of pages5
JournalPhysics & Imaging in Radiation Oncology
Publication statusPublished - Oct 2020


  • Artificial intelligence
  • Machine learning
  • Radiotherapy
  • Survey
  • Commissioning
  • Quality assurance
  • Clinical implementation

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