Online treatment adaptation strategies for the 1.5T MR-linac: first implementation and evaluation for lymph node oligometastases

Dennis Winkel

Research output: ThesisDoctoral thesis 1 (Research UU / Graduation UU)


Treatment planning is an important aspect of radiotherapy to make sure adequate target coverage is achieved, without compromising tolerances of surrounding healthy tissue. Automated treatment planning has contributed to reduced variance and higher efficiency and plays a role in certain supporting aspects such as quality assurance and decision making. Image guided radiotherapy (IGRT) ensures accurate delivery of the treatment plan on the intended location. The introduction of MR-guidance in radiotherapy has shifted the paradigm and, compared to conventional radiotherapy treatment, opened up numerous opportunities for online treatment planning and plan adaptation based on the actual patient anatomy. In Chapter 2 a two-phased planning and optimization workflow was developed to automatically generate 77Gy 5-field simultaneously integrated boost intensity modulated radiation therapy (SIB-IMRT) plans for prostate cancer treatment. The automated radiotherapy treatment planning and optimization workflow is capable of efficiently generating patient specifically optimized and improved clinical grade plans. It has now been adopted as the current standard workflow in our clinic to generate treatment plans for prostate cancer. The promise of the MR-linac is that one can visualize all anatomical changes during the course of radiotherapy and hence adapt the treatment plan in order to always have the optimal treatment. Yet, there is a trade-off to be made between the time spent for adapting the treatment plan against the dosimetric gain. In Chapter 3, the various daily plan adaptation methods was presented and applied on five cases with varying levels of inter-fraction motion, regions of interest and target sizes. Chapter 4 investigated the dosimetric impact of online replanning for SBRT of lymph node oligometastases as a method for correcting for inter-fraction anatomical changes. The use of online replanning based on the actual anatomy as seen on repeated MRI compared to online position correction for lymph node oligometastases SBRT gives beneficial dosimetric outcomes and reduces the amount of unplanned violations of dose constraints. In Chapter 5 we determined the optimal plan adaptation approach for MR-guided SBRT treatment of lymph node oligometastases. The most advanced method, in which a full online replanning was performed by segment shape and weight optimization after fluence optimization, yielded the most favourable dosimetric values and could be performed within a time-frame acceptable (<5 min) for MR-guided treatment. Chapter 6 investigates different online treatment planning strategies and the planning target volume (PTV) margin needed for adequate target coverage when treating lymph node oligometastases with SBRT on the 1.5T MR-linac. The applied 3 mm isotropic PTV margin is considered adequate. For pelvic and para-aortic metastatic lymph nodes, online MRI-guided adaptive treatment planning results in adequate PTV and GTV coverage when taking the actual patient anatomy into account (ATS). Generally, GTV coverage remained adequate throughout the treatment session for both adaptive planning strategies. Finally, in Chapter 7 the dosimetric benefit of the first 1.5T MR-linac SBRT treatments of single and multiple lymph node oligometastases compared to conventional CBCT-linac treatment was determined. The largest benefit was gained in patients treated for multiple lymph node oligometastases.
Original languageEnglish
Awarding Institution
  • University Medical Center (UMC) Utrecht
  • Raaymakers, Bas, Primary supervisor
  • Bol, G.H., Co-supervisor
  • Kroon, P.S., Co-supervisor
Award date17 Oct 2019
Place of Publication[Utrecht]
Print ISBNs978-946323-831-1
Publication statusPublished - 17 Oct 2019


  • radiotherapy
  • MRI
  • lymph node
  • oligometastases
  • cancer
  • MR-linac
  • treatment planning
  • online adaption
  • autoplanning
  • automated treatment planning


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