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Multi-criteria optimization will change the way you plan

A constant difficulty encountered in radiation therapy treatment planning is the patient-specific trade-off between ensuring appropriate tumor coverage and avoiding excessive radiation to healthy structures. Such trade-offs are conventionally resolved by manually altering an optimization problem formulation and re-optimizing the treatment plan multiple times. Trial and error of this form is time consuming and even if a treatment plan deemed satisfactory is found, it is not clear if better treatment options exist for the current patient. With rayNavigator, the multi-criteria optimization method in RayStation you can simplify your planning process while improving the quality of your plans.

Watch how RISO experiences working with multi-criteria optimization

RISO Radiotherapeutic Institute in the Netherlands is one of the first RayStation users. In this video, they describe their partnership with RaySearch and how multi-criteria optimization in RayStation has helped them improve the workflow and personalize care for patients.

Multi-criteria optimization/ rayNavigator

rayNavigator introduces the concept of multi-criteria optimization (MCO), which provides an alternative optimization workflow. Instead of the planner performing iterative optimization with adjustments to optimization functions and weights RayStation generates a set of Pareto plans.

These plans respect all constraints and no objectives can be improved without impairing another one. Based on these plans, the planner or physician can manipulate sliders in real time to balance between clinical trade-offs.

  • An interactive navigation tool for selecting the best clinical trade-off based on fluence-based anchor plans.
  • Automatic tool for generating a deliverable treatment plan from this selection.
  • rayNavigator is available for IMRT, VMAT and Proton PBS planning.
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Directly deliverable VMAT plans

The RayStation 8B release introduces a segment-based optimization mode for VMAT, using sliding window sequencing, where Pareto plans are generated by direct machine parameter optimization. The deliverable plan is created by control point interpolation, resulting in a high level of agreement between the navigated dose and the dose of the deliverable plan. This new optimization mode is available for Elekta Agility and Elekta MLCi2.

"The typical trade-off in radiation treatment planning is target coverage versus critical structure sparing. Traditional treatment planning proceeds by a trial and error fashion, where the planner tries to guess at system optimization parameters that might strike the best balance amongst the multiple conflicting goals. But this process can be quite time consuming. Multi-Criteria Optimization simplifies this by presenting the planner with a set of sliders which allow them to surf across the trade-off space and quickly decide on the right balance."

David Craft

Assistant Professor at the Department of Radiation Oncology, Massachusetts General Hospital in Boston.

"Planning is one of the most important things in radiation oncology, because you want to treat the tumor right, and you don’t want to give a high dose to the normal tissue. Multi-Criteria Optimization in RayStation allows us to balance the clinical trade-offs in real time to reach a more personalized treatment plan for the patient."

Sandrine van de Pol

Radiation oncologist, Radiotherapiegroep, Deventer, The Netherlands.

Clinical benefits

  • Planners and physicians can find solutions they didn’t know existed.
  • Physicians tend to select plans with higher OAR sparing at the expense of slightly under dosing target as they can see exactly where it happen.
  • The total treatment planning time is significantly reduced without compromising plan quality.
  • Planners with limited experience and knowledge can produce clinically acceptable plans.

Watch a short demonstration of rayNavigator

Watch an in-depth demonstration of MCO