MR-guided radiotherapy requires novel quality assurance (QA) methods for intensity-modulated radiotherapy treatment plans (TPs). Here, an optimized method for TPs for a 1.5 T MR-linac was developed and implemented clinically. A static solid phantom and an MR-compatible 2D ionization chamber array were used. The array’s response with respect to the incident beam gantry angles was characterized for four different orientations of the array relative to the beam. A lookup table was created identifying the optimum orientation for each gantry angle. For the QA of clinical MR-linac TPs, beams were grouped according to their gantry angles and measured with up to four setups. The method was applied to n = 106 clinical TPs of 54 patients for different tumour entities. Reference plans and plans created in the online adaptive workflow were analysed, using a local 3%/3 mm gamma criterion for dose values larger than 30% of the maximum. Pass rates were averaged over all beam groups. The array’s response strongly depends on the beam incidence angle. Optimum angles typically range from −10° to 80° around the phantom setup angle. Consequently, plan verification required up to four setups. For clinical MR-linac TPs, the overall median pass rate was 98.5% (range 88.6%–100%). Pass rates depended on the tumour entity. Median pass rates were for liver metastases stereotactic body radiotherapy 99.2%, prostate cancer 99%, pancreatic cancer 98.9%, lymph node metastases 98.7%, partial breast irradiation (PBI) 98%, head-and-neck cancer 97.7%, rectal cancer 94% and others 96.6%. 85% of plans were accepted straightaway, with pass rates above 95%. A single plan with a pass rate below 90% was subsequently verified with a modified method. Off-axis target volumes, e.g. PBI, were verified successfully using a lateral shift of the phantom. The method is suitable to verify reference and online adapted TPs for a 1.5 T MR-linac, including plans for off-axis target volumes.

Introduction: Stereotactic body radiotherapy (SBRT) is an established ablative treatment for liver tumors with excellent local control rates. Magnetic resonance imaging guided radiotherapy (MRgRT) provides superior soft tissue contrast and may therefore facilitate a marker-less liver SBRT workflow. The goal of the present study was to investigate feasibility, workflow parameters, toxicity and patient acceptance of MRgSBRT on a 1.5 T MR-Linac. Methods: Ten consecutive patients with liver metastases treated on a 1.5 T MR-Linac were included in this prospective trial. Tumor delineation was performed on four-dimensional computed tomography scans and both exhale triggered and free-breathing T2 MRI scans from the MR-Linac. An internal target volume based approach was applied. Organ at risk constraints were based on the UKSABR guidelines (Version 6.1). Patient acceptance regarding device specific aspects was assessed and toxicity was scored according to the common toxicity criteria of adverse events, version 5. Results: Nine of ten tumors were clearly visible on the 1.5 T MR-Linac. No patient had fiducial markers placed for treatment. All patients were treated with three or five fractions. Median dose to 98% of the gross tumor volume was 38.5 Gy. The median time from ''patient identity check" until ''beam-off" was 31 min. Median beam on time was 9.6 min. Online MRgRT was well accepted in general and no treatment had to be interrupted on patient request. No event of symptomatic radiation induced liver disease was observed after a median follow-up of ten month (range 3-17 months). Conclusion: Our early experience suggests that online 1.5 T MRgSBRT of liver metastases represents a promising new non-invasive marker-free treatment modality based on high image quality, clinically reasonable in-room times and high patient acceptance. Further studies are necessary to assess clinical outcome, to validate advanced motion management and to explore the benefit of online response adaptive liver SBRT.

Background and purpose: Currently clinical radiotherapy (RT) planning consists of a multi-step routine procedure requiring human interaction which often results in a time-consuming and fragmented process with limited robustness. Here we present an autonomous un-supervised treatment planning approach, integrated as basis for online adaptive magnetic resonance guided RT (MRgRT), which was delivered to a prostate cancer patient as a first-in-human experience. Materials and methods: For an intermediate risk prostate cancer patient OARs and targets were automatically segmented using a deep learning-based software and logical volume operators. A baseline plan for the 1.5 T MR-Linac (20x3 Gy) was automatically generated using particle swarm optimization (PSO) without any human interaction. Plan quality was evaluated by predefined dosimetric criteria including appropriate tolerances. Online plan adaptation during clinical MRgRT was defined as first checkpoint for human interaction. Results: OARs and targets were successfully segmented (3 min) and used for automatic plan optimization (300 min). The autonomous generated plan satisfied 12/16 dosimetric criteria, however all remained within tolerance. Without prior human validation, this baseline plan was successfully used during online MRgRT plan adaptation, where 14/16 criteria were fulfilled. As postulated, human interaction was necessary only during plan adaptation. Conclusion: Autonomous, un-supervised data preparation and treatment planning was first-in-human shown to be feasible for adaptive MRgRT and successfully applied. The checkpoint for first human intervention was at the time of online MRgRT plan adaptation. Autonomous planning reduced the time delay between simulation and start of RT and may thus allow for real-time MRgRT applications in the future.

To investigate the potential of Particle Swarm Optimization (PSO) for fully automatic VMAT radiotherapy (RT) treatment planning. Material and Methods: In PSO a solution space of planning constraints is searched for the best possible RT plan in an iterative, statistical method, optimizing a population of candidate solutions. To identify the best candidate solution and for final evaluation a plan quality score (PQS), based on dose volume histogram (DVH) parameters, was introduced. Automatic PSO-based RT planning was used for N = 10 postoperative prostate cancer cases, retrospectively taken from our clinical database, with a prescribed dose of EUD = 66 Gy in addition to two constraints for rectum and one for bladder. Resulting PSO-based plans were compared dosimetrically to manually generated VMAT plans. Results: PSO successfully proposed treatment plans comparable to manually optimized ones in 9/10 cases. The median (range) PTV EUD was 65.4 Gy (64.7-66.0) for manual and 65.3 Gy (62.5-65.5) for PSO plans, respectively. However PSO plans achieved significantly lower doses in rectum D 2% 67.0 Gy (66.5-67.5) vs. 66.1 Gy (64.7-66.5, p = 0.016). All other evaluated parameters (PTV D 98% and D 2% , rectum V 40Gy and V 60Gy , bladder D 2% and V 60Gy) were comparable in both plans. Manual plans had lower PQS compared to PSO plans with −0.82 (−16.43-1.08) vs. 0.91 (−5.98-6.25). Conclusion: PSO allows for fully automatic generation of VMAT plans with plan quality comparable to manually optimized plans. However, before clinical implementation further research is needed concerning further adaptation of PSO-specific parameters and the refinement of the PQS.

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