- Email: [email protected]
SP-0589 MCO FOR IMRT PLANNING
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diseases, based on prognostic characteristics that need consideration in clinical trials. Uncertainty exists about how to exploit stratification to optimize treatment de-intensification vs. intensification strategies for OPC patients in different risk categories. Well designed randomized trials are needed to address potential de-intensification strategies appropriate for different subsets of the HPV(+) population. Clinical trial strategies that have commenced or are in development include the introduction of epidermal grown factor receptor (EGFR) targeting as a potential substitute for cisplatin in multimodality approaches and the use of induction chemotherapy strategies and EGFR monoclonal antibody targeting to identify patients who may be treated with lower doses of radiotherapy. In favorable cases, the use of conventional altered fractionation radiotherapy alone or trans-oral resection with or without less intense radiotherapy and systemic agents delivered as adjuvants have also been suggested. Generally speaking for the HPV(-) patients, intensification probably needs to be maintained to realize optimal outcome, although here selection also needs to become more refined. This is especially the case when we consider the burgeoning population of older patients who may not tolerate traditional treatments and may also not respond to them effectively.
SYMPOSIUM: ADVANCES IN DOSE CALCULATION AND OPTIMISATION ALGORITHMS. IMPROVING PLANNING EFFICACITY AND PLAN QUALITY SP-0589 MCO FOR IMRT PLANNING D. Craft1, T. Bortfeld1 1 Mass. General Hospital, Department of Radiation Oncology, Boston MA, USA In this talk we will review the state of MCO clinical planning at the Massachusetts General Hospital. We will review best-practice problem formulations for prostate, head and neck, brain, and lung planning. We will also go over pitfalls and shortcomings of the current clinical MCO implementation, and avenues for fixing these, including future research topics. One of the subtler challenges which will be discussed is the following: even with a Pareto-surface based multi-criteria system, it is not clear how to present tradeoffs in treatment planning. Planners using the MCO system still often have the feeling that two (potentially conflicting) objectives can be improved simultaneously. In IMRT planning, what would it take to have a system that would definitively, and in near real time, be able to address such issues? SP-0590 MULTI-CRITERIA OPTIMIZATION IN RADIATION ONCOLOGY: FROM PRIORISATION TO INTERACTION A. Schlaefer1 1University of Luebeck, Medical Robotics, Institute for Robotics and Cognitive Systems Purpose: Conventionally, the balance of different planning criteria is controlled by coefficients in the objective function of the underlying optimization problem. However, the fundamental conflict between the criteria remains and the purpose of treatment planning must be to find the best trade-off. Using methods for constrained optimization can make this process more systematic and speed up the search for an acceptable plan. Material & methods: We compared different approaches to realize multi-criteria optimization (MCO) based on constrained optimization. The core idea is to express clinical planning criteria in terms of dose bounds and consider each criterion separately. Including the empty solution, i.e., no dose, guarantees feasibility of the problem. Feasibility is maintained by adding new, stricter bounds only as a result of an optimization, i.e., when the bounds are fulfilled by the actual plan. Hence, instead of estimating the complete solution space in advance, it is explored by running a sequence of optimization steps. A first approach is to define a set of desired bounds ordered by their importance for the clinical problem. The optimization problem is sequentially solved with respect to each bound, and after each optimization step constraints are added to preserve the plan quality. While the approach has practical merits, there are also some limitations. A strict order is implied even if this may not be obvious for the clinical criteria, and the resulting solution is not necessarily optimal. Hence, a second method is to allow any sequence of optimization steps, including repeated optimizations with respect to
ESTRO 31
the same bound. Still, only one criterion is optimized at a time and we refer to this approach as stepwise multi-criteria optimization (SMCO). Clearly, bounds can always be relaxed without affecting feasibility and a combination of relaxation and optimization steps can be used to balance criteria without stating a clear order of importance. This turns planning into a systematic and interactive search for a Pareto optimal solution. Moreover, criteria typically not reflected by explicit constraints can be considered by the planner, e.g., the shape of the dose distribution, even outside volumes of interest. This motivates a further approach: the direct interaction with the three-dimensional dose distribution to analyze possible trade-offs. Based on a fast solution of the underlying optimization problem all constraints limiting better plan quality are highlighted. This feedback allows identifying where to relax, i.e., the best compromise with respect to the spatial dose distribution can be studied. Results: We have implemented methods for sequential optimization of an ordered set of optimization steps, interactive SMCO, and the interactive optimization of the spatial trade-off. The first method is particularly useful for automatic planning, e.g., according to clinical guidelines. It is used in at least one commercial system (MultiPlan® Treatment Planning System, Accuray Incorporated). As a practical advantage the optimization steps do not depend on the patient geometry, leading to consistent plans that present a good solution and also a starting point for further optimization. The latter can be achieved using interactive SMCO, which allows studying the best patient specific trade-off among the criteria and to establish Paretooptimality. Using MCO to shape the spatial dose distribution can be useful when steep gradients are essential, e.g., in radiosurgery. Currently the runtime for typical optimization steps ranges from several seconds to some minutes. Conclusion: Treatment planning for radiation therapy has to balance conflicting goals. MCO presents a useful and practical approach to systematically search for the best clinical trade-off. While methods based on pre-computing a set of potential solutions allow faster analysis, online optimization facilitates a more interactive approach with the potential to consider the spatial dose distribution. SP-0591 INTRODUCTION TO GRID-BASED SOLVERS OF BOLTZMANN TRANSPORT EQUATION O.N. Vassiliev1 1 Tom Baker Cancer Centre, Medical Physics, Calgary, Canada Recently a new approach was introduced to dose calculations for radiotherapy treatment planning. It is called grid-based Boltzmann solvers (GBBS). Superior performance of the first algorithm of this type was demonstrated in my papers [1, 2] which resulted in its almost immediate commercialization. It is now offered by Varian Medical Systems as an advanced dose algorithm Acuros. There is a version of the algorithm for brachytherapy (Acuros BV) and for external beam (Acuros XB). Despite clear and proven advantage of GBBS in terms of speed and accuracy, research on algorithms of this type is minimal. It is mostly limited to further evaluation of Acuros performance. The reason may be that this approach is based on numerical methods new to the field and less intuitive than, for example, Monte Carlo. These methods, however, have been used for decades in engineering and nuclear physics, and they are not as complicated as some publications tend to present. The purpose of my presentation is to describe this approach in simple terms with focus on the underlying physics and general workflow. It will include a discussion on underlying approximations of Boltzmann transport equation, physical meaning of individual terms in the equation, its form in mixed radiation fields such as those in external beam therapy. Then, discretization of the equation will be described. Because particle state is specified by three types of variables, spatial, angular and energy, three different discretization techniques are used: finite elements (Galerkin method) for spatial variables, discrete ordinates for angles and a multi-group approximation for energy. Additionally, for angular variables Legendre series and spherical harmonics are used to simplify the collision integral. The discretized Boltzmann equation is solved by an iterative procedure comprising two nested loops. The inner loop is called source iterations. In this loop a solution is sought for one energy group, i.e. energy is fixed. At each step of source iterations fluence found in a preceding step is substituted in the collision integral and the equation is solved. Iterations begin with the unscattered fluence. At each step a system of linear algebraic equation is solved for fluence at each node of the spatial grid for each
diseases, based on prognostic characteristics that need consideration in clinical trials. Uncertainty exists about how to exploit stratification to optimize treatment de-intensification vs. intensification strategies for OPC patients in different risk categories. Well designed randomized trials are needed to address potential de-intensification strategies appropriate for different subsets of the HPV(+) population. Clinical trial strategies that have commenced or are in development include the introduction of epidermal grown factor receptor (EGFR) targeting as a potential substitute for cisplatin in multimodality approaches and the use of induction chemotherapy strategies and EGFR monoclonal antibody targeting to identify patients who may be treated with lower doses of radiotherapy. In favorable cases, the use of conventional altered fractionation radiotherapy alone or trans-oral resection with or without less intense radiotherapy and systemic agents delivered as adjuvants have also been suggested. Generally speaking for the HPV(-) patients, intensification probably needs to be maintained to realize optimal outcome, although here selection also needs to become more refined. This is especially the case when we consider the burgeoning population of older patients who may not tolerate traditional treatments and may also not respond to them effectively.
SYMPOSIUM: ADVANCES IN DOSE CALCULATION AND OPTIMISATION ALGORITHMS. IMPROVING PLANNING EFFICACITY AND PLAN QUALITY SP-0589 MCO FOR IMRT PLANNING D. Craft1, T. Bortfeld1 1 Mass. General Hospital, Department of Radiation Oncology, Boston MA, USA In this talk we will review the state of MCO clinical planning at the Massachusetts General Hospital. We will review best-practice problem formulations for prostate, head and neck, brain, and lung planning. We will also go over pitfalls and shortcomings of the current clinical MCO implementation, and avenues for fixing these, including future research topics. One of the subtler challenges which will be discussed is the following: even with a Pareto-surface based multi-criteria system, it is not clear how to present tradeoffs in treatment planning. Planners using the MCO system still often have the feeling that two (potentially conflicting) objectives can be improved simultaneously. In IMRT planning, what would it take to have a system that would definitively, and in near real time, be able to address such issues? SP-0590 MULTI-CRITERIA OPTIMIZATION IN RADIATION ONCOLOGY: FROM PRIORISATION TO INTERACTION A. Schlaefer1 1University of Luebeck, Medical Robotics, Institute for Robotics and Cognitive Systems Purpose: Conventionally, the balance of different planning criteria is controlled by coefficients in the objective function of the underlying optimization problem. However, the fundamental conflict between the criteria remains and the purpose of treatment planning must be to find the best trade-off. Using methods for constrained optimization can make this process more systematic and speed up the search for an acceptable plan. Material & methods: We compared different approaches to realize multi-criteria optimization (MCO) based on constrained optimization. The core idea is to express clinical planning criteria in terms of dose bounds and consider each criterion separately. Including the empty solution, i.e., no dose, guarantees feasibility of the problem. Feasibility is maintained by adding new, stricter bounds only as a result of an optimization, i.e., when the bounds are fulfilled by the actual plan. Hence, instead of estimating the complete solution space in advance, it is explored by running a sequence of optimization steps. A first approach is to define a set of desired bounds ordered by their importance for the clinical problem. The optimization problem is sequentially solved with respect to each bound, and after each optimization step constraints are added to preserve the plan quality. While the approach has practical merits, there are also some limitations. A strict order is implied even if this may not be obvious for the clinical criteria, and the resulting solution is not necessarily optimal. Hence, a second method is to allow any sequence of optimization steps, including repeated optimizations with respect to
ESTRO 31
the same bound. Still, only one criterion is optimized at a time and we refer to this approach as stepwise multi-criteria optimization (SMCO). Clearly, bounds can always be relaxed without affecting feasibility and a combination of relaxation and optimization steps can be used to balance criteria without stating a clear order of importance. This turns planning into a systematic and interactive search for a Pareto optimal solution. Moreover, criteria typically not reflected by explicit constraints can be considered by the planner, e.g., the shape of the dose distribution, even outside volumes of interest. This motivates a further approach: the direct interaction with the three-dimensional dose distribution to analyze possible trade-offs. Based on a fast solution of the underlying optimization problem all constraints limiting better plan quality are highlighted. This feedback allows identifying where to relax, i.e., the best compromise with respect to the spatial dose distribution can be studied. Results: We have implemented methods for sequential optimization of an ordered set of optimization steps, interactive SMCO, and the interactive optimization of the spatial trade-off. The first method is particularly useful for automatic planning, e.g., according to clinical guidelines. It is used in at least one commercial system (MultiPlan® Treatment Planning System, Accuray Incorporated). As a practical advantage the optimization steps do not depend on the patient geometry, leading to consistent plans that present a good solution and also a starting point for further optimization. The latter can be achieved using interactive SMCO, which allows studying the best patient specific trade-off among the criteria and to establish Paretooptimality. Using MCO to shape the spatial dose distribution can be useful when steep gradients are essential, e.g., in radiosurgery. Currently the runtime for typical optimization steps ranges from several seconds to some minutes. Conclusion: Treatment planning for radiation therapy has to balance conflicting goals. MCO presents a useful and practical approach to systematically search for the best clinical trade-off. While methods based on pre-computing a set of potential solutions allow faster analysis, online optimization facilitates a more interactive approach with the potential to consider the spatial dose distribution. SP-0591 INTRODUCTION TO GRID-BASED SOLVERS OF BOLTZMANN TRANSPORT EQUATION O.N. Vassiliev1 1 Tom Baker Cancer Centre, Medical Physics, Calgary, Canada Recently a new approach was introduced to dose calculations for radiotherapy treatment planning. It is called grid-based Boltzmann solvers (GBBS). Superior performance of the first algorithm of this type was demonstrated in my papers [1, 2] which resulted in its almost immediate commercialization. It is now offered by Varian Medical Systems as an advanced dose algorithm Acuros. There is a version of the algorithm for brachytherapy (Acuros BV) and for external beam (Acuros XB). Despite clear and proven advantage of GBBS in terms of speed and accuracy, research on algorithms of this type is minimal. It is mostly limited to further evaluation of Acuros performance. The reason may be that this approach is based on numerical methods new to the field and less intuitive than, for example, Monte Carlo. These methods, however, have been used for decades in engineering and nuclear physics, and they are not as complicated as some publications tend to present. The purpose of my presentation is to describe this approach in simple terms with focus on the underlying physics and general workflow. It will include a discussion on underlying approximations of Boltzmann transport equation, physical meaning of individual terms in the equation, its form in mixed radiation fields such as those in external beam therapy. Then, discretization of the equation will be described. Because particle state is specified by three types of variables, spatial, angular and energy, three different discretization techniques are used: finite elements (Galerkin method) for spatial variables, discrete ordinates for angles and a multi-group approximation for energy. Additionally, for angular variables Legendre series and spherical harmonics are used to simplify the collision integral. The discretized Boltzmann equation is solved by an iterative procedure comprising two nested loops. The inner loop is called source iterations. In this loop a solution is sought for one energy group, i.e. energy is fixed. At each step of source iterations fluence found in a preceding step is substituted in the collision integral and the equation is solved. Iterations begin with the unscattered fluence. At each step a system of linear algebraic equation is solved for fluence at each node of the spatial grid for each