Early experience with MR-guided adaptive radiotherapy using a 1.5 T MR-Linac: First 6 months of operation using adapt to shape workflow

J Med Imaging Radiat Oncol. 2022 Feb;66(1):138-145. doi: 10.1111/1754-9485.13336. Epub 2021 Oct 12.

Abstract

Introduction: The magnetic resonance linear accelerator (MRL) offers improved soft tissue visualization to guide daily adaptive radiotherapy treatment. This manuscript aims to report initial experience using a 1.5 T MRL in the first 6 months of operation, including training, workflows, timings and dosimetric accuracy.

Methods: All staff received training in MRI safety and MRL workflows. Initial sites chosen for treatment were stereotactic and hypofractionated prostate, thoraco-abdomino-pelvic metastasis, prostate bed and bladder. The Adapt To Shape (ATS) workflow was chosen to be the focus of treatment as it is the most robust solution for daily adaptive radiotherapy. A workflow was created addressing patient suitability, simulation, planning, treatment and peer review. Treatment times were recorded breaking down into the various stages of treatment.

Results: A total of 37 patients were treated and 317 fractions delivered (of which 313 were delivered using an ATS workflow) in our initial 6 months. Average treatment times over the entire period were 50 and 38 min for stereotactic and non-stereotactic treatments respectively. Average treatment times reduced each month. The average difference between reference planned and ionization chamber measured dose was 0.0 ± 1.4%.

Conclusion: The MRL was successfully established in an Australian setting. A focus on training and creating a detailed workflow from patient selection, review and treatment are paramount to establishing new treatment programmes.

Keywords: MR-Linac; MRL; adaptive radiatiotherapy; radiotherapy; technology implementation.

MeSH terms

  • Australia
  • Humans
  • Magnetic Resonance Imaging
  • Male
  • Particle Accelerators
  • Radiotherapy Dosage
  • Radiotherapy Planning, Computer-Assisted*
  • Radiotherapy, Image-Guided*
  • Workflow