On the Importance of Individualized, Non-Coplanar Beam Configurations in Mediastinal Lymphoma Radiotherapy, Optimized With Automated Planning

Rossi, L.; Lopes, P. Cambraia; Leitão, Joana; Janus, Cécile P.M.; Pol, M. van de; Breedveld, S.; Penninkhof, J.; Heijmen, B.

doi:10.3389/fonc.2021.619929

Cited by 10 publications

(10 citation statements)

References 44 publications

(57 reference statements)

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“…Advanced options for beam angle optimization are currently lacking in commercial TPSs. Many studies have demonstrated the benefit of such algorithms for non-coplanar treatment (5)(6)(7)(8)(9)(10)(11)28). This study demonstrates that such algorithms could also enhance treatment plan quality for coplanar MRL treatments.…”

Section: Discussionmentioning

confidence: 62%

MR-Linac Radiotherapy – The Beam Angle Selection Problem

et al. 2021

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BackgroundWith the large-scale introduction of volumetric modulated arc therapy (VMAT), selection of optimal beam angles for coplanar static-beam IMRT has increasingly become obsolete. Due to unavailability of VMAT in current MR-linacs, the problem has re-gained importance. An application for automated IMRT treatment planning with integrated, patient-specific computer-optimization of beam angles (BAO) was used to systematically investigate computer-aided generation of beam angle class solutions (CS) for replacement of computationally expensive patient-specific BAO. Rectal cancer was used as a model case.Materials and Methods23 patients treated at a Unity MR-linac were included. BAOx plans (x=7-12 beams) were generated for all patients. Analyses of BAO12 plans resulted in CSx class solutions. BAOx plans, CSx plans, and plans with equi-angular setups (EQUIx, x=9-56) were mutually compared.ResultsFor x>7, plan quality for CSx and BAOx was highly similar, while both were superior to EQUIx. E.g. with CS9, bowel/bladder Dmean reduced by 22% [11%, 38%] compared to EQUI9 (p<0.001). For equal plan quality, the number of EQUI beams had to be doubled compared to BAO and CS.ConclusionsComputer-generated beam angle CS could replace individualized BAO without loss in plan quality, while reducing planning complexity and calculation times, and resulting in a simpler clinical workflow. CS and BAO largely outperformed equi-angular treatment. With the developed CS, time consuming beam angle re-optimization in daily adaptive MR-linac treatment could be avoided. Further systematic research on computerized development of beam angle class solutions for MR-linac treatment planning is warranted.

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Section: Discussionmentioning

confidence: 62%

MR-Linac Radiotherapy – The Beam Angle Selection Problem

et al. 2021

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“…This BAO approach was chosen because it has been shown to provide good quality treatment plans in previous studies. 18,32,[44][45][46] Alternatively, the proposed workflow could also be combined with a BAO approach that selects beam angles prior to FMO optimization. This would avoid the need for multiple FMO iterations, which would reduce the computation time.…”

Section: Discussionmentioning

confidence: 99%

“…Contributing most to the computation time was the applied integrated, iterative BAO which is computationally expensive for large numbers of beams. This BAO approach was chosen because it has been shown to provide good quality treatment plans in previous studies 18,32,44–46 . Alternatively, the proposed workflow could also be combined with a BAO approach that selects beam angles prior to FMO optimization.…”

Section: Discussionmentioning

confidence: 99%

Fully automated treatment planning for MLC‐based robotic radiotherapy

et al. 2021

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To propose and validate a fully automated multi-criterial treatment planning solution for a CyberKnife ® equipped with an InCise™ 2 multi-leaf collimator. Methods: The AUTO BAO plans are generated with fully-automated prioritized multi-criterial optimization (AUTO MCO) of pencil-beam fluence maps with integrated non-coplanar beam angle optimization (BAO), followed by MLC segment generation. Both the AUTO MCO and segmentation algorithms have been in-house developed. AUTO MCO generates for each patient a single, high-quality Pareto-optimal IMRT plan. The segmentation algorithm then accurately mimics the AUTO MCO 3D dose distribution, while considering all candidate beams simultaneously, rather than replicating the fluence maps. Pencil-beams, segment dose depositions, and final dose calculations are performed with a stand-alone version of the clinical dose calculation engine. For validation, AUTO BAO plans were generated for 33 prostate SBRT patients and compared to reference plans (REF) that were manually generated with the commercial treatment planning system (TPS), in absence of time pressure. REF plans were also compared to AUTO RB plans, for which fluence map optimization was performed for the beam angle configuration used in the REF plan, and the segmentation could use all these beams or only a subset, depending on the dosimetry. Results: AUTO BAO plans were clinically acceptable and dosimetrically similar to REF plans, but had on average reduced numbers of beams ((beams in AUTO BAO)/(beams in REF) (relative improvement): 24.7/48.3 (-49%)), segments (59.5/98.9 (-40%)), and delivery times (17.1/22.3 min. (-23%)). Dosimetry of AUTO RB and REF were also similar, but AUTO RB used on average fewer beams (38.0/48.3 (-21%)) and had on average shorter delivery times (18.6/22.3 min. (-17%)). Delivered Monitor Units (MU) were similar for all three planning approaches. Conclusions: A new, vendor-independent optimization workflow for fully automated generation of deliverable high-quality CyberKnife ® plans was proposed, including BAO. Compared to manual planning with the commercial TPS, fraction delivery times i Accepted ArticleThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

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“…All plans used for this study were automatically generated with Erasmus-iCycle [14,15]. For many tumor sites, this system has demonstrated superiority of automatically generated plans, over manually generated plans [14,[16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: System For Automated Plan Generationmentioning

confidence: 99%