| 1. |
- Bengtsson, Ivar, et al.
(författare)
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Implications of using the clinical target distribution as voxel-weights in radiation therapy optimization
- 2023
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Ingår i: Physics in Medicine and Biology. - : IOP Publishing. - 0031-9155 .- 1361-6560. ; 68:9
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Tidskriftsartikel (refereegranskat)abstract
- Objective. Delineating and planning with respect to regions suspected to contain microscopic tumor cells is an inherently uncertain task in radiotherapy. The recently proposed clinical target distribution (CTD) is an alternative to the conventional clinical target volume (CTV), with initial promise. Previously, using theCTDin planning has primarily been evaluated in comparison to a conventionally defined CTV. Wepropose to compare theCTDapproach against CTVmargins of various sizes, dependent on the threshold at which the tumor infiltration probability is considered relevant. Approach. First, a theoretical framework is presented, concerned with optimizing the trade-off between the probability of sufficient target coverage and the penalties associated with high dose. From this framework we derive conventional CTV-based planning and contrast it with theCTDapproach. The approaches are contextualized further by comparison with established methods for managing geometric uncertainties. Second, for both one- and three-dimensional phantoms, we compare a set of CTDplans created by varying the target objective function weight against a set of plans created by varying both the target weight and the CTVmargin size. Main results. The results show that CTD-based planning gives slightly inefficient trade-offs between the evaluation criteria for a case in which near-minimum target dose is the highest priority. However, in a case when sparing a proximal organ at risk is critical, theCTDis better at maintaining sufficiently high dose toward the center of the target. Significance. Weconclude that CTD-based planning is a computationally efficient method for planning with respect to delineation uncertainties, but that the inevitable effects on the dose distribution should not be disregarded.
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| 2. |
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| 3. |
- Bokrantz, Rasmus, 1981-, et al.
(författare)
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Necessary and sufficient conditions for Pareto efficiency in robust multiobjective optimization
- 2017
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Ingår i: European Journal of Operational Research. - : ELSEVIER SCIENCE BV. - 0377-2217 .- 1872-6860. ; 262:2, s. 682-692
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Tidskriftsartikel (refereegranskat)abstract
- We provide necessary and sufficient conditions for robust efficiency (in the sense of Ehrgott et al., 2014) to multiobjective optimization problems that depend on uncertain parameters. These conditions state that a solution is robust efficient (under minimization) if it is optimal to a strongly increasing scalarizing function, and only if it is optimal to a strictly increasing scalarizing function. By counterexample, we show that the necessary condition cannot be strengthened to convex scalarizing functions, even for convex problems. We therefore define and characterize a subset of the robust efficient solutions for which an analogous necessary condition holds with respect to convex scalarizing functions. This result parallels the deterministic case where optimality to a convex and strictly increasing scalarizing function constitutes a necessary condition for efficiency. By a numerical example from the field of radiation therapy treatment plan optimization, we illustrate that the curvature of the scalarizing function influences the conservatism of an optimized solution in the uncertain case.
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| 4. |
- Fredriksson, Albin
(författare)
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A characterization of robust radiation therapy treatment planning methods-from expected value to worst case optimization
- 2012
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Ingår i: Medical physics (Lancaster). - : Wiley. - 0094-2405. ; 39:8, s. 5169-5181
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To characterize a class of optimization formulations used to handle systematic and random errors in radiation therapy, and to study the differences between the methods within this class. Methods: The class of robust methods that can be formulated as minimax stochastic programs is studied. This class generalizes many previously used methods, ranging between optimization of the expected and the worst case objective value. The robust methods are used to plan intensity-modulated proton therapy (IMPT) treatments for a case subject to systematic setup and range errors, random setup errors with and without uncertain probability distribution, and combinations thereof. As reference, plans resulting from a conventional method that uses a margin to account for errors are shown. Results: For all types of errors, target coverage robustness increased with the conservativeness of the method. For systematic errors, best case organ at risk (OAR) doses increased and worst case doses decreased with the conservativeness. Accounting for random errors of fixed probability distribution resulted in heterogeneous dose. The heterogeneities were reduced when uncertainty in the probability distribution was accounted for. Doing so, the OAR doses decreased with the conservativeness. All robust methods studied resulted in more robust target coverage and lower OAR doses than the conventional method. Conclusions: Accounting for uncertainties is essential to ensure plan quality in complex radiation therapy such as IMPT. The utilization of more information than conventional in the optimization can lead to robust target coverage and low OAR doses. Increased target coverage robustness can be achieved by more conservative methods.
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| 5. |
- Fredriksson, Albin
(författare)
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Automated improvement of radiation therapy treatment plans by optimization under reference dose constraints
- 2012
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Ingår i: Physics in Medicine and Biology. - : IOP Publishing. - 0031-9155 .- 1361-6560. ; 57:23, s. 7799-7811
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Tidskriftsartikel (refereegranskat)abstract
- A method is presented that automatically improves upon previous treatment plans by optimization under reference dose constraints. In such an optimization, a previous plan is taken as reference and a new optimization is performed toward some goal, such as minimization of the doses to healthy structures under the constraint that no structure can become worse off than in the reference plan. Two types of constraints that enforce this are discussed: either each voxel or each dose-volume histogram of the improved plan must be at least as good as in the reference plan. These constraints ensure that the quality of the dose distribution cannot deteriorate, something that constraints on conventional physical penalty functions do not. To avoid discontinuous gradients, which may restrain gradient-based optimization algorithms, the positive part operators that constitute the optimization functions are regularized. The method was applied to a previously optimized plan for a C-shaped phantom and the effects of the choice of regularization parameter were studied. The method resulted in reduced integral dose and reduced doses to the organ at risk while maintaining target homogeneity. It could be used to improve upon treatment plans directly or as a means of quality control of plans.
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| 6. |
- Fredriksson, Albin, et al.
(författare)
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Deliverable navigation for multicriteria IMRT treatment planning by combining shared and individual apertures
- 2013
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Ingår i: Physics in Medicine and Biology. - : IOP Publishing. - 0031-9155 .- 1361-6560. ; 58:21, s. 7683-7697
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Tidskriftsartikel (refereegranskat)abstract
- We consider the problem of deliverable Pareto surface navigation for step-and-shoot intensity-modulated radiation therapy. This problem amounts to calculation of a collection of treatment plans with the property that convex combinations of plans are directly deliverable. Previous methods for deliverable navigation impose restrictions on the number of apertures of the individual plans, or require that all treatment plans have identical apertures. We introduce simultaneous direct step-and-shoot optimization of multiple plans subject to constraints that some of the apertures must be identical across all plans. This method generalizes previous methods for deliverable navigation to allow for treatment plans with some apertures from a collective pool and some apertures that are individual. The method can also be used as a post-processing step to previous methods for deliverable navigation in order to improve upon their plans. By applying the method to subsets of plans in the collection representing the Pareto set, we show how it can enable convergence toward the unrestricted (non-navigable) Pareto set where all apertures are individual.
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| 8. |
- Fredriksson, Albin, et al.
(författare)
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Maximizing the probability of satisfying the clinical goals in radiation therapy treatment planning under setup uncertainty
- 2015
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Ingår i: Medical physics (Lancaster). - : Wiley. - 0094-2405. ; 42:7, s. 3992-3999
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: This paper introduces a method that maximizes the probability of satisfying the clinical goals in intensity-modulated radiation therapy treatments subject to setup uncertainty. Methods: The authors perform robust optimization in which the clinical goals are constrained to be satisfied whenever the setup error falls within an uncertainty set. The shape of the uncertainty set is included as a variable in the optimization. The goal of the optimization is to modify the shape of the uncertainty set in order to maximize the probability that the setup error will fall within the modified set. Because the constraints enforce the clinical goals to be satisfied under all setup errors within the uncertainty set, this is equivalent to maximizing the probability of satisfying the clinical goals. This type of robust optimization is studied with respect to photon and proton therapy applied to a prostate case and compared to robust optimization using an a priori defined uncertainty set. Results: Slight reductions of the uncertainty sets resulted in plans that satisfied a larger number of clinical goals than optimization with respect to a priori defined uncertainty sets, both within the reduced uncertainty sets and within the a priori, nonreduced, uncertainty sets. For the prostate case, the plans taking reduced uncertainty sets into account satisfied 1.4 (photons) and 1.5 (protons) times as many clinical goals over the scenarios as the method taking a priori uncertainty sets into account. Conclusions: Reducing the uncertainty sets enabled the optimization to find better solutions with respect to the errors within the reduced as well as the nonreduced uncertainty sets and thereby achieve higher probability of satisfying the clinical goals. This shows that asking for a little less in the optimization sometimes leads to better overall plan quality.
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| 9. |
- Fredriksson, Albin, 1984-, et al.
(författare)
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Maximizing the probability of satisfying the planning criteria in radiation therapy under setup uncertainty
- 2013
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- We consider intensity-modulated photon and proton therapy in the presence of setup uncertainty. The uncertainty is accounted for by worst case optimization, in which the planning criteria are constrained to be satisfied under all setup errors from a specified set. To handle that the set may contain errors under which the planning criteria cannot be satisfied, a method is introduced that includes the magnitudes of the setup errors within the set as variables in the optimization, which is aimed at making these magnitudes as large as possible (within specified bounds) while satisfying all planning criteria under the errors. This is equivalent to maximizing the probability of satisfying the planning criteria.The method is studied with respect to photon and proton therapy applied to a prostate case and a lung case, and compared to worst case optimization with respect to an a priori determined set of errors. For both modalities, slight reductions of the magnitudes of the considered setup errors resulted in plans that satisfied a substantially larger number of planning criteria under the retracted errors, and also a larger number of criteria under the a priori errors: for the prostate case, the plans accounting for retracted errors satisfied 1.5 (photons) and 1.2 (protons) times as many planning criteria as the method accounting for a priori errors, and for the lung case, the numbers were 2.7 (photons) and 1.6 (protons).
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| 10. |
- Fredriksson, Albin, et al.
(författare)
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Minimax optimization for handling range and setup uncertainties in proton therapy
- 2011
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Ingår i: Medical physics (Lancaster). - : Wiley. - 0094-2405. ; 38:3, s. 1672-1684
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Intensity modulated proton therapy (IMPT) is sensitive to errors, mainly due to high stopping power dependency and steep beam dose gradients. Conventional margins are often insufficient to ensure robustness of treatment plans. In this article, a method is developed that takes the uncertainties into account during the plan optimization. Methods: Dose contributions for a number of range and setup errors are calculated and a minimax optimization is performed. The minimax optimization aims at minimizing the penalty of the worst case scenario. Any optimization function from conventional treatment planning can be utilized by the method. By considering only scenarios that are physically realizable, the unnecessary conservativeness of other robust optimization methods is avoided. Minimax optimization is related to stochastic programming by the more general minimax stochastic programming formulation, which enables accounting for uncertainties in the probability distributions of the errors. Results: The minimax optimization method is applied to a lung case, a paraspinal case with titanium implants, and a prostate case. It is compared to conventional methods that use margins, single field uniform dose (SFUD), and material override (MO) to handle the uncertainties. For the lung case, the minimax method and the SFUD with MO method yield robust target coverage. The minimax method yields better sparing of the lung than the other methods. For the paraspinal case, the minimax method yields more robust target coverage and better sparing of the spinal cord than the other methods. For the prostate case, the minimax method and the SFUD method yield robust target coverage and the minimax method yields better sparing of the rectum than the other methods. Conclusions: Minimax optimization provides robust target coverage without sacrificing the sparing of healthy tissues, even in the presence of low density lung tissue and high density titanium implants. Conventional methods using margins, SFUD, and MO do not utilize the full potential of IMPT and deliver unnecessarily high doses to healthy tissues.
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