Iterative parameter tuning and multiple re-optimization is no longer necessary with multi-criteria optimization in RayStation. This module allows for the continuous exploration of the possible treatment options in real time so you can find the precise balance for every patient between target coverage and the sparing of healthy structures.
RISO Radiotherapeutic Institute in the Netherlands is one of the first RayStation users. In this short video, the RISO team describes its partnership with RaySearch and how multi-criteria optimization in RayStation has helped improve workflows and more personalized patient care.
Multi-criteria optimization offers oncology teams an alternative to traditional optimization workflows.
Avoid iterative optimization with adjustments to functions and weights with multi-criteria optimization in RayStation. This module instead allows Pareto optimal treatment plans to be generated according to user-specified objectives and constraints.
Planners and physicians can finetune treatment plans by moving sliders in real time to find the right balance between conflicting clinical goals. The plan remains Pareto optimal with all constraints respected – no objectives can be improved without negatively impacting others. Pre-computation of all Pareto optimal plans can be fully automated, so the planner and physician can explore different solutions in a joint meeting without being interrupted by time-consuming calculations.
Deliverable sliding is supported for VMAT, DMLC, TomoTherapy and proton PBS. Plan exploration includes deliverable plans being generated with extremely narrow approximation to the navigate dose.
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In this video Rasmus Bokrantz, Head of Algorithm at RaySearch presents multi-criteria optimization in RayStation. RaySearch was first to introduce MCO in a treatment planning system in 2011 and since then we have improved it with every release. MCO allows the oncology team (planners and physicians) to finetune the treatment plan in real time, using sliders to directly see the outcome of balance between conflicting and clinical goals. Watch the video to learn more.
The segment-based optimization mode for VMAT uses sliding window sequencing and generates Pareto optimal plans by direct machine parameter optimization. Deliverable plans are created by control point interpolation, resulting in a high level of agreement between the navigated dose and the dose of the deliverable plan.
Planners and physicians can find solutions they did not know existed. Hong et al. 2008, Müller et al. 2017
Physicians tend to select plans with higher OAR sparing at the expense of slightly under dosing target as they can see exactly where it happens. Kamran et al. 2016, Wala et al. 2013
The total treatment planning time is significantly reduced without compromising plan quality. Craft et al. 2012, Kamran et al. 2016
Planners with limited experience and knowledge can produce clinically acceptable plans. Kierkels et al. 2015
“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.
