Reduced-order constrained optimization in IMRT planning

Lu, Renzhi; Radke, Richard J.; Yang, Jie; Happersett, Laura; Yorke, Ellen; Jackson, Andrew

doi:10.1088/0031-9155/53/23/007

Cited by 15 publications

(11 citation statements)

References 24 publications

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“…The ROCO method has been described in detail in previous work, 1 and is briefly summarized here. The method consists of three major phases as follows.…”

Section: Iib the Roco Methodsmentioning

confidence: 99%

“…In previous work, we proposed the reduced order constrained optimization (ROCO) method that applies machine learning techniques to IMRT treatment planning, demonstrating its success in generating acceptable plans for the prostate 1 and lung 2 sites. In this paper, we extend ROCO to the headand-neck (H&N) site, which is substantially more complex given the numerous organs at risk (OARs) involved.…”

Section: Introductionmentioning

confidence: 99%

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Reduced-order constrained optimization (ROCO): Clinical application to head-and-neck IMRT

et al. 2013

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Purpose: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. Methods: 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). Results: 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. Conclusions: 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.

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“…The ROCO method has been described in detail in previous work, 1 and is briefly summarized here. The method consists of three major phases as follows.…”

Section: Iib the Roco Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reduced-order constrained optimization (ROCO): Clinical application to head-and-neck IMRT

et al. 2013

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“…II, we briefly review each of these steps, placing emphasis on the new features we have added; a more complete treatment is given in our previous paper. 4 …”

Section: Figmentioning

confidence: 99%

“…The dimension of the space of possible treatments is larger for these locally advanced lung cases than for prostate cases, because the larger treatment fields contain a greater total number of beamlets. For prostate cases there are on the order of 10 3 beamlets, and for the lung cases that we consider, there are about 10 4 . Finally, IMRT for NSCLC often includes "rind" structures to prevent hot spots in nonspecific normal tissues.…”

Section: Introductionmentioning

confidence: 99%

“…By minimizing the trial-and-error effort characteristic of current IMRT planning, it allows treatment planners to focus on clinical tradeoffs between tumor coverage and normal organ doses. We have previously applied ROCO to prostate cancer cases; 4 in this paper, we improve our application of ROCO and report new results on a more challenging treatment site, the lung.…”

Section: Introductionmentioning

confidence: 99%

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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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Treatment Planning Protocols: A Method to Improve Consistency in IMRT Planning

Bahm

Montgomery

2011

Medical Dosimetry

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Reduced-order constrained optimization in IMRT planning

Cited by 15 publications

References 24 publications

Reduced-order constrained optimization (ROCO): Clinical application to head-and-neck IMRT

Reduced-order constrained optimization (ROCO): Clinical application to head-and-neck IMRT

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

Treatment Planning Protocols: A Method to Improve Consistency in IMRT Planning

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