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

ABSTRACT: 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.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.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.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.

SUBMITTER: Stabenau H

PROVIDER: S-EPMC3107833 | biostudies-other | 2011 May

REPOSITORIES: biostudies-other

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Project description:The authors present the application of the reduced order constrained optimization (ROCO) method, previously successfully applied to the prostate and lung sites, to the head-and-neck (H&N) site, demonstrating that it can quickly and automatically generate clinically competitive IMRT plans. We provide guidelines for applying ROCO to larynx, oropharynx, and nasopharynx cases, and report the results of a live experiment that demonstrates how an expert planner can save several hours of trial-and-error interaction using the proposed approach.The ROCO method used for H&N IMRT planning consists of three major steps. First, the intensity space of treatment plans is sampled by solving a series of unconstrained optimization problems with a parameter range based on previously treated patient data. Second, the dominant modes in the intensity space are estimated by dimensionality reduction using principal component analysis (PCA). Third, a constrained optimization problem over this basis is quickly solved to find an IMRT plan that meets organ-at-risk (OAR) and target coverage constraints. The quality of the plan is assessed using evaluation tools within Memorial Sloan-Kettering Cancer Center (MSKCC)'s treatment planning system (TPS).The authors generated ten H&N IMRT plans for previously treated patients using the ROCO method and processed them for deliverability by a dynamic multileaf collimator (DMLC). The authors quantitatively compared the ROCO plans to the previously achieved clinical plans using the TPS tools used at MSKCC, including DVH and isodose contour analysis, and concluded that the ROCO plans would be clinically acceptable. In our current implementation, ROCO H&N plans can be generated using about 1.6 h of offline computation followed by 5-15 min of semiautomatic planning time. Additionally, the authors conducted a live session for a plan designated by MSKCC performed together with an expert H&N planner. A technical assistant set up the first two steps, which were performed without further human interaction, and then collaborated in a virtual meeting with the expert planner to perform the third (constrained optimization) step. The expert planner performed in-depth analysis of the resulting ROCO plan and deemed it to be clinically acceptable and in some aspects superior to the clinical plan. This entire process took 135 min including two constrained optimization runs, in comparison to the estimated 4 h that would have been required using traditional clinical planning tools.The H&N site is very challenging for IMRT planning, due to several levels of prescription and a large, variable number (6-20) of OARs that depend on the location of the tumor. ROCO for H&N shows promise in generating clinically acceptable plans both more quickly and with substantially less human interaction.

Project description:The current inverse planning methods for intensity modulated radiation therapy (IMRT) are limited because they are not designed to explore the trade-offs between the competing objectives of tumor and normal tissues. The goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans.A hierarchical evolutionary multiobjective algorithm designed to quickly generate a small diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the optimal trade-offs in any radiation therapy plan was developed. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. The population size is not fixed, but a specialized niche effect, domination advantage, is used to control the population and plan diversity. The number of fitness objectives is kept to a minimum for greater selective pressure, but the number of genes is expanded for flexibility that allows a better approximation of the Pareto front.The MOEA improvements were evaluated for two example prostate cases with one target and two organs at risk (OARs). The population of plans generated by the modified MOEA was closer to the Pareto front than populations of plans generated using a standard genetic algorithm package. Statistical significance of the method was established by compiling the results of 25 multiobjective optimizations using each method. From these sets of 12-15 plans, any random plan selected from a MOEA population had a 11.3% +/- 0.7% chance of dominating any random plan selected by a standard genetic package with 0.04% +/- 0.02% chance of domination in reverse. By implementing domination advantage and protocol objectives, small and diverse populations of clinically acceptable plans that approximated the Pareto front could be generated in a fraction of 1 h. Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for multiobjective optimizations.The MOEA produces a diverse Pareto optimal set of plans that meet all dosimetric protocol criteria in a feasible amount of time. The final goal is to improve practical aspects of the algorithm and integrate it with a decision analysis tool or human interface for selection of the IMRT plan with the best possible balance of successful treatment of the target with low OAR dose and low risk of complication for any specific patient situation.

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

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