Costlets: A Generalized Approach to Cost Functions for Automated Optimization of IMRT Treatment Plans

Kessler, Marc L.; McShan, Daniel L.; Epelman, Marina A.; Vineberg, K.A.; Eisbruch, Avraham; Lawrence, Theodore S.; Fraass, Benedick A.

doi:10.1007/s11081-005-2066-2

Cited by 52 publications

(49 citation statements)

References 74 publications

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“…A typical scalar objective function for IMRT planning is composed of several dose-and dose-volume-based terms (i.e., costlets (Kessler et al, 2005)). Each term includes a dose parameter (i.e., a minimum or maximum limit) or a pair of dose-volume parameters (i.e., a point on a dose-volume-histogram (DVH) curve), and reflects a clinical objective.…”

Section: Tunable Parameters In Imrt Objective Functionsmentioning

confidence: 99%

Reduced-order parameter optimization for simplifying prostate IMRT planning

Radke

Happersett

et al. 2007

Phys. Med. Biol.

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Abstract.Intensity-modulated radiotherapy (IMRT) has become an effective tool for cancer treatment with radiation. However, even expert radiation planners still need to spend a substantial amount of time manually adjusting IMRT optimization parameters such as dose limits and costlet weights in order to obtain a clinically acceptable plan. In this paper, we describe two main advances that simplify the parameter adjustment process for five-field prostate IMRT planning. First, we report the results of a sensitivity analysis that quantifies the effect of each hand-tunable parameter of the IMRT cost function on each clinical objective and the overall quality of the resulting plan. Second, we show that a recursive random search over the 6 most sensitive parameters as an outer loop in IMRT planning can quickly and automatically determine parameters for the cost function that lead to a plan meeting the clinical requirements. Our experiments on a 10-patient dataset show that for 70% of the cases, we can automatically determine a plan in 10 minutes (on the average) that is either clinically acceptable or requires only minor adjustment by the planner. The outer loop optimization can be easily integrated into a traditional IMRT planning system.

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Section: Tunable Parameters In Imrt Objective Functionsmentioning

confidence: 99%

Reduced-order parameter optimization for simplifying prostate IMRT planning

Radke

Happersett

et al. 2007

Phys. Med. Biol.

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“…[8][9][10][11] The basic characteristics of this system have been previously described. The current version consists of software developed in FORTRAN, C, and Cþþ, and makes use of GKS and OpenGL for graphics, as well as a special version of AVS.…”

Section: Iia Optimization Systemmentioning

confidence: 99%

“…Various search algorithms for the beamlet intensities are implemented, but for this work, only a quasiNewton gradient method is used. All objective functions for the optimization use the costlet functionality described by Kessler et al 11 Since the beamlet IMRT plans are intended to demonstrate the optimal result that can be achieved with IMRT, the MLC sequencing is performed with a large number of segments (typically up to 100 per beam) so that the deliverable IMRT plan achieves intensities with very little degradation due to the sequencing step (note: this is also our clinical practice). As has been shown in a number of papers, modulation and the number of MUs in the field are controlled using a number of different methods, including limiting the maximum beamlet intensity, 18 penalizing high modulation changes, 19 and through use of adaptive diffusion smoothing.…”

Section: Iic Beamlet-based Optimizationmentioning

confidence: 99%

Inverse‐optimized 3D conformal planning: Minimizing complexity while achieving equivalence with beamlet IMRT in multiple clinical sites

et al. 2012

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Purpose: Inverse planned intensity modulated radiation therapy (IMRT) has helped many centers implement highly conformal treatment planning with beamlet-based techniques. The many comparisons between IMRT and 3D conformal (3DCRT) plans, however, have been limited because most 3DCRT plans are forward-planned while IMRT plans utilize inverse planning, meaning both optimization and delivery techniques are different. This work avoids that problem by comparing 3D plans generated with a unique inverse planning method for 3DCRT called inverse-optimized 3D (IO-3D) conformal planning. Since IO-3D and the beamlet IMRT to which it is compared use the same optimization techniques, cost functions, and plan evaluation tools, direct comparisons between IMRT and simple, optimized IO-3D plans are possible. Though IO-3D has some similarity to direct aperture optimization (DAO), since it directly optimizes the apertures used, IO-3D is specifically designed for 3DCRT fields (i.e., 1-2 apertures per beam) rather than starting with IMRT-like modulation and then optimizing aperture shapes. The two algorithms are very different in design, implementation, and use. The goals of this work include using IO-3D to evaluate how close simple but optimized IO-3D plans come to nonconstrained beamlet IMRT, showing that optimization, rather than modulation, may be the most important aspect of IMRT (for some sites). Methods: The IO-3D dose calculation and optimization functionality is integrated in the in-house 3D planning/optimization system. New features include random point dose calculation distributions, costlet and cost function capabilities, fast dose volume histogram (DVH) and plan evaluation tools, optimization search strategies designed for IO-3D, and an improved, reimplemented edge/octree calculation algorithm. The IO-3D optimization, in distinction to DAO, is designed to optimize 3D conformal plans (one to two segments per beam) and optimizes MLC segment shapes and weights with various user-controllable search strategies which optimize plans without beamlet or pencil beam approximations. IO-3D allows comparisons of beamlet, multisegment, and conformal plans optimized using the same cost functions, dose points, and plan evaluation metrics, so quantitative comparisons are straightforward. Here, comparisons of IO-3D and beamlet IMRT techniques are presented for breast, brain, liver, and lung plans. Results: IO-3D achieves high quality results comparable to beamlet IMRT, for many situations. Though the IO-3D plans have many fewer degrees of freedom for the optimization, this work finds that IO-3D plans with only one to two segments per beam are dosimetrically equivalent (or nearly so) to the beamlet IMRT plans, for several sites. IO-3D also reduces plan complexity significantly. Here, monitor units per fraction (MU/Fx) for IO-3D plans were 22%-68% less than that for the 1 cm Â 1 cm beamlet IMRT plans and 72%-84% than the 0.5 cm Â 0.5 cm beamlet IMRT plans. Conclusions: The unique IO-3D algorithm illustrates that inverse planning can achieve ...

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“…The standard clinical approach to inverse IMRT planning is to combine all the evaluation criteria specified by the physician into a scalar value using a weighted sum of several terms (i.e., costlets 8 ). Each term includes a dose parameter (i.e., a minimum or maximum limit) or a pair of dose-volume parameters (i.e., a point on a DVH curve), and reflects a clinical objective.…”

Section: Figmentioning

confidence: 99%

Reduced order constrained optimization (ROCO): Clinical application to lung IMRT

et al. 2011

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Purpose: The authors use reduced-order constrained optimization (ROCO) to create clinically acceptable IMRT plans quickly and automatically for advanced lung cancer patients. Their new ROCO implementation works with the treatment planning system and full dose calculation used at Memorial Sloan-Kettering Cancer Center (MSKCC). The authors have implemented mean dose hard constraints, along with the point-dose and dose-volume constraints that the authors used for our previous work on the prostate. Methods: ROCO consists of three major steps. First, the space of treatment plans is sampled by solving a series of optimization problems using penalty-based quadratic objective functions. Next, an efficient basis for this space is found via principal component analysis (PCA); this reduces the dimensionality of the problem. Finally, a constrained optimization problem is solved over this basis to find a clinically acceptable IMRT plan. Dimensionality reduction makes constrained optimization computationally efficient. Results: The authors apply ROCO to 12 stage III non-small-cell lung cancer (NSCLC) cases, generating IMRT plans that meet all clinical constraints and are clinically acceptable, and demonstrate that they are competitive with the clinical treatment plans. The authors also test how many samples and PCA modes are necessary to achieve an adequate lung plan, demonstrate the importance of long-range dose calculation for ROCO, and evaluate the performance of nonspecific normal tissue ("rind") constraints in ROCO treatment planning for the lung. Finally, authors show that ROCO can save time for planners, and they estimate that in the clinic, planning using their approach would save a median of 105 min for the patients in the study. Conclusions: New challenges arise when applying ROCO to the lung site, which include the lack of a class solution, a larger treatment site, an increased number of parameters and beamlets, a variable number of beams and beam arrangement, and the customary use of rinds in clinical plans to avoid high-dose areas outside the PTV. In the authors previous work, use of an approximate dose calculation in the hard constraint optimization sometimes meant that clinical constraints were not met when evaluated with the full dose calculation. This difficulty has been removed in the current work by using the full dose calculation in the hard constraint optimization. The authors have demonstrated that ROCO offers a fast and automatic way to create IMRT plans for advanced NSCLC, which extends their previous application of ROCO to prostate cancer IMRT planning.

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Costlets: A Generalized Approach to Cost Functions for Automated Optimization of IMRT Treatment Plans

Cited by 52 publications

References 74 publications

Reduced-order parameter optimization for simplifying prostate IMRT planning

Reduced-order parameter optimization for simplifying prostate IMRT planning

Inverse‐optimized 3D conformal planning: Minimizing complexity while achieving equivalence with beamlet IMRT in multiple clinical sites

Reduced order constrained optimization (ROCO): Clinical application to lung IMRT

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