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Cao R, Si L, Li X, Guang Y, Wang C, Tian Y, Pei X, Zhang X. A conjugate gradient-assisted multi-objective evolutionary algorithm for fluence map optimization in radiotherapy treatment. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00697-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractIntensity-modulated radiotherapy (IMRT) is one of the most applied techniques for cancer radiotherapy treatment. The fluence map optimization is an essential part of IMRT plan designing, which has a significant impact on the radiotherapy treatment effect. In fact, the treatment planing of IMRT is an inverse multi-objective optimization problem. Existing approaches of solving the fluence map optimization problem (FMOP) obtain a satisfied treatment plan via trying different coupling weights, the optimization process needs to be conducted many times and the coupling weight setting is completely based on the experience of a radiation physicist. For fast obtaining diverse high-quality radiotherapy plans, this paper formulates the FMOP into a three-objective optimization problem, and proposes a conjugate gradient-assisted multi-objective evolutionary algorithm (CG-MOEA) to solve it. The proposed algorithm does not need to set the coupling weights and can produce the diverse radiotherapy plans within a single run. Moreover, the convergence speed is further accelerated by an adaptive local search strategy based on the conjugate-gradient method. Compared with five state-of-the-art multi-objective evolutionary algorithms (MOEAs), the proposed CG-MOEA can obtain the best hypervolume (HV) values and dose–volume histogram (DVH) performance on five clinical cases in cancer radiotherapy. Moreover, the proposed algorithm not only obtains the more optimal solution than traditional method used to solve the FMOP, but also can find diverse Pareto solution set which can be provided to radiation physicist to select the best treatment plan. The proposed algorithm outperforms dose-volume histogram state-of-the-art multi-objective evolutionary algorithms and traditional method for FMOP on five clinical cases in cancer radiotherapy.
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Zarepisheh M, Hong L, Zhou Y, Huang Q, Yang J, Jhanwar G, Pham HD, Dursun P, Zhang P, Hunt MA, Mageras GS, Yang JT, Yamada Y, Deasy JO. Automated and Clinically Optimal Treatment Planning for Cancer Radiotherapy. INFORMS JOURNAL ON APPLIED ANALYTICS 2022; 52:69-89. [PMID: 35847768 PMCID: PMC9284667 DOI: 10.1287/inte.2021.1095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Each year, approximately 18 million new cancer cases are diagnosed worldwide, and about half must be treated with radiotherapy. A successful treatment requires treatment planning with the customization of penetrating radiation beams to sterilize cancerous cells without harming nearby normal organs and tissues. This process currently involves extensive manual tuning of parameters by an expert planner, making it a time-consuming and labor-intensive process, with quality and immediacy of critical care dependent on the planner's expertise. To improve the speed, quality, and availability of this highly specialized care, Memorial Sloan Kettering Cancer Center developed and applied advanced optimization tools to this problem (e.g., using hierarchical constrained optimization, convex approximations, and Lagrangian methods). This resulted in both a greatly improved radiotherapy treatment planning process and the generation of reliable and consistent high-quality plans that reflect clinical priorities. These improved techniques have been the foundation of high-quality treatments and have positively impacted over 4,000 patients to date, including numerous patients in severe pain and in urgent need of treatment who might have otherwise required longer hospital stays or undergone unnecessary surgery to control the progression of their disease. We expect that the wide distribution of the system we developed will ultimately impact patient care more broadly, including in resource-constrained countries.
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Affiliation(s)
- Masoud Zarepisheh
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York
| | - Linda Hong
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York
| | - Ying Zhou
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York
| | - Qijie Huang
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York
| | - Jie Yang
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York
| | - Gourav Jhanwar
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York
| | - Hai D Pham
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York
| | - Pinar Dursun
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York
| | - Pengpeng Zhang
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York
| | - Margie A Hunt
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York
| | - Gig S Mageras
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York
| | - Jonathan T Yang
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York
| | - Yoshiya Yamada
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York
| | - Joseph O Deasy
- Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, New York
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Huang C, Yang Y, Panjwani N, Boyd S, Xing L. Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface. IEEE Trans Biomed Eng 2021; 68:2907-2917. [PMID: 33523802 PMCID: PMC8526351 DOI: 10.1109/tbme.2021.3055822] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Radiation therapy treatment planning is a time-consuming, iterative process with potentially high inter-planner variability. Fully automated treatment planning processes could reduce a planner's active treatment planning time and remove inter-planner variability, with the potential to tremendously improve patient turnover and quality of care. In developing fully automated algorithms for treatment planning, we have two main objectives: to produce plans that are 1) Pareto optimal and 2) clinically acceptable. Here, we propose the Pareto optimal projection search (POPS) algorithm, which provides a general framework for directly searching the Pareto front. METHODS Our POPS algorithm is a novel automated planning method that combines two main search processes: 1) gradient-free search in the decision variable space and 2) projection of decision variables to the Pareto front using the bisection method. We demonstrate the performance of POPS by comparing with clinical treatment plans. As one possible quantitative measure of treatment plan quality, we construct a clinical acceptability scoring function (SF) modified from the previously developed general evaluation metric (GEM). RESULTS On a dataset of 21 prostate cases collected as part of clinical workflow, our proposed POPS algorithm produces Pareto optimal plans that are clinically acceptable in regards to dose conformity, dose homogeneity, and sparing of organs-at-risk. CONCLUSION Our proposed POPS algorithm provides a general framework for fully automated treatment planning that achieves clinically acceptable dosimetric quality without requiring active planning from human planners. SIGNIFICANCE Our fully automated POPS algorithm addresses many key limitations of other automated planning approaches, and we anticipate that it will substantially improve treatment planning workflow.
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Paganetti H, Beltran C, Both S, Dong L, Flanz J, Furutani K, Grassberger C, Grosshans DR, Knopf AC, Langendijk JA, Nystrom H, Parodi K, Raaymakers BW, Richter C, Sawakuchi GO, Schippers M, Shaitelman SF, Teo BKK, Unkelbach J, Wohlfahrt P, Lomax T. Roadmap: proton therapy physics and biology. Phys Med Biol 2021; 66. [DOI: 10.1088/1361-6560/abcd16] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 11/23/2020] [Indexed: 12/12/2022]
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Taasti VT, Hong L, Shim JSA, Deasy JO, Zarepisheh M. Automating proton treatment planning with beam angle selection using Bayesian optimization. Med Phys 2020; 47:3286-3296. [PMID: 32356335 DOI: 10.1002/mp.14215] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 04/19/2020] [Accepted: 04/21/2020] [Indexed: 11/06/2022] Open
Abstract
PURPOSE To present a fully automated treatment planning process for proton therapy including beam angle selection using a novel Bayesian optimization approach and previously developed constrained hierarchical fluence optimization method. METHODS We adapted our in-house automated intensity modulated radiation therapy (IMRT) treatment planning system, which is based on constrained hierarchical optimization and referred to as ECHO (expedited constrained hierarchical optimization), for proton therapy. To couple this to beam angle selection, we propose using a novel Bayesian approach. By integrating ECHO with this Bayesian beam selection approach, we obtain a fully automated treatment planning framework including beam angle selection. Bayesian optimization is a global optimization technique which only needs to search a small fraction of the search space for slowly varying objective functions (i.e., smooth functions). Expedited constrained hierarchical optimization is run for some initial beam angle candidates and the resultant treatment plan for each beam configuration is rated using a clinically relevant treatment score function. Bayesian optimization iteratively predicts the treatment score for not-yet-evaluated candidates to find the best candidate to be optimized next with ECHO. We tested this technique on five head-and-neck (HN) patients with two coplanar beams. In addition, tests were performed with two noncoplanar and three coplanar beams for two patients. RESULTS For the two coplanar configurations, the Bayesian optimization found the optimal beam configuration after running ECHO for, at most, 4% of all potential configurations (23 iterations) for all patients (range: 2%-4%). Compared with the beam configurations chosen by the planner, the optimal configurations reduced the mandible maximum dose by 6.6 Gy and high dose to the unspecified normal tissues by 3.8 Gy, on average. For the two noncoplanar and three coplanar beam configurations, the algorithm converged after 45 iterations (examining <1% of all potential configurations). CONCLUSIONS A fully automated and efficient treatment planning process for proton therapy, including beam angle optimization was developed. The algorithm automatically generates high-quality plans with optimal beam angle configuration by combining Bayesian optimization and ECHO. As the Bayesian optimization is capable of handling complex nonconvex functions, the treatment score function which is used in the algorithm to evaluate the dose distribution corresponding to each beam configuration can contain any clinically relevant metric.
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Affiliation(s)
- Vicki T Taasti
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Linda Hong
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Masoud Zarepisheh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Taasti VT, Hong L, Deasy JO, Zarepisheh M. Automated proton treatment planning with robust optimization using constrained hierarchical optimization. Med Phys 2020; 47:2779-2790. [PMID: 32196679 DOI: 10.1002/mp.14148] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 03/02/2020] [Accepted: 03/11/2020] [Indexed: 11/06/2022] Open
Abstract
PURPOSE We present a method for fully automated generation of high quality robust proton treatment plans using hierarchical optimization. To fill the gap between the two common extreme robust optimization approaches, that is, stochastic and worst-case, a robust optimization approach based on the p-norm function is used whereby a single parameter, p , can be used to control the level of robustness in an intuitive way. METHODS A fully automated approach to treatment planning using Expedited Constrained Hierarchical Optimization (ECHO) is implemented in our clinic for photon treatments. ECHO strictly enforces critical (inviolable) clinical criteria as hard constraints and improves the desirable clinical criteria sequentially, as much as is feasible. We extend our in-house developed ECHO codes for proton therapy and integrate it with a new approach for robust optimization. Multiple scenarios accounting for both setup and range uncertainties are included (13scenarios), and the maximum/mean/dose-volume constraints on organs-at-risk (OARs) and target are fulfilled in all scenarios. We combine the objective functions of the individual scenarios using the p-norm function. The p-norm with a parameter p = 1 or p = ∞ result in the stochastic or the worst-case approach, respectively; an intermediate robustness level is obtained by employing p -values in-between. While the worst-case approach only focuses on the worst-case scenario(s), the p-norm approach with a large p value ( p ≈ 20 ) resembles the worst-case approach without completely neglecting other scenarios. The proposed approach is evaluated on three head-and-neck (HN) patients and one water phantom with different parameters, p ∈ 1 , 2 , 5 , 10 , 20 . The results are compared against the stochastic approach (p-norm approach with p = 1 ) and the worst-case approach, as well as the nonrobust approach (optimized solely on the nominal scenario). RESULTS The proposed algorithm successfully generates automated robust proton plans on all cases. As opposed to the nonrobust plans, the robust plans have narrower dose volume histogram (DVH) bands across all 13 scenarios, and meet all hard constraints (i.e., maximum/mean/dose-volume constraints) on OARs and the target for all scenarios. The spread in the objective function values is largest for the stochastic approach ( p = 1 ) and decreases with increasing p toward the worst-case approach. Compared to the worst-case approach, the p-norm approach results in DVH bands for clinical target volume (CTV) which are closer to the prescription dose at a negligible cost in the DVH for the worst scenario, thereby improving the overall plan quality. On average, going from the worst-case approach to the p-norm approach with p = 20 , the median objective function value across all the scenarios is improved by 15% while the objective function value for the worst scenario is only degraded by 3%. CONCLUSION An automated treatment planning approach for proton therapy is developed, including robustness, dose-volume constraints, and the ability to control the robustness level using the p-norm parameter p , to fit the priorities deemed most important.
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Affiliation(s)
- Vicki T Taasti
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Linda Hong
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Masoud Zarepisheh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Wang C, Zhu X, Hong JC, Zheng D. Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future. Technol Cancer Res Treat 2020; 18:1533033819873922. [PMID: 31495281 PMCID: PMC6732844 DOI: 10.1177/1533033819873922] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works.
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Affiliation(s)
- Chunhao Wang
- 1 Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Xiaofeng Zhu
- 2 Department of Radiation Oncology, Georgetown University Hospital, Rockville, MD, USA
| | - Julian C Hong
- 1 Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.,3 Department of Radiation Oncology, University of California, San Francisco, CA, USA
| | - Dandan Zheng
- 4 Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
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Mukherjee S, Hong L, Deasy JO, Zarepisheh M. Integrating soft and hard dose-volume constraints into hierarchical constrained IMRT optimization. Med Phys 2019; 47:414-421. [PMID: 31742731 DOI: 10.1002/mp.13908] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 09/30/2019] [Accepted: 10/30/2019] [Indexed: 11/11/2022] Open
Abstract
PURPOSE Dose-volume constraints (DVCs) continue to be common features in intensity-modulated radiation therapy (IMRT) prescriptions, but they are non-convex and difficult to incorporate. We propose computationally efficient methods to incorporate dose-volume constraints (DVCs) into automated IMRT planning. METHODS We propose a two-phase approach: in phase-1, we solve a convex approximation with DVCs. Although this convex approximation does not guarantee DVC satisfaction, it provides crucial initial information about voxels likely to receive doses below DVC thresholds. Subsequently, phase-2 solves an optimization problem with maximum dose constraints imposed on those subthreshold voxels. We further categorize DVCs into hard- and soft-DVCs, where hard-DVCs are strictly enforced by the optimization and soft-DVCs are encouraged in the objective function. We tested this approach in our automated treatment planning system which is based on hierarchical constrained optimization. Performance is demonstrated on a series of paraspinal, lung, oligometastasis, and prostate cases as well as a small paraspinal case for which we can computationally afford to obtain a ground-truth by solving a non-convex optimization problem. RESULTS The proposed algorithm successfully meets all the hard-DVCs while increasing the overall computational time of the baseline planning process (without DVCs) by 20%, 10%, and 11% for paraspinal, oligometastasis, and prostate cases, respectively. For a soft-DVC applied to the lung case, the dose-volume histogram curve moves toward the desired direction and the computational time is increased by 11%. For a low-resolution paraspinal case, the ground-truth solution process using mixed-integer programming methods required 15 h while the proposed algorithm converges in only 2 min with a proximal solution. CONCLUSIONS A computationally tractable algorithm to handle hard- and soft-DVCs is developed which is capable of satisfying DVCs without any parameter tweaking. Although the algorithm is demonstrated in our in-house developed automated treatment planning system, it can potentially be used in any constrained optimization framework.
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Affiliation(s)
- Sovanlal Mukherjee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Linda Hong
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Masoud Zarepisheh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Hong L, Zhou Y, Yang J, Mechalakos JG, Hunt MA, Mageras GS, Yang J, Yamada J, Deasy JO, Zarepisheh M. Clinical Experience of Automated SBRT Paraspinal and Other Metastatic Tumor Planning With Constrained Hierarchical Optimization. Adv Radiat Oncol 2019; 5:1042-1050. [PMID: 33083666 PMCID: PMC7557131 DOI: 10.1016/j.adro.2019.11.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 11/12/2019] [Accepted: 11/26/2019] [Indexed: 11/18/2022] Open
Abstract
Purpose We report on the clinical performance of a fully automated approach to treatment planning based on a Pareto optimal, constrained hierarchical optimization algorithm, named Expedited Constrained Hierarchical Optimization (ECHO). Methods and materials From April 2017 to October 2018, ECHO produced 640 treated plans for 523 patients who underwent stereotactic body radiation therapy (RT) for paraspinal and other metastatic tumors. A total of 182 plans were for 24 Gy in a single fraction, 387 plans were for 27 Gy in 3 fractions, and the remainder were for other prescriptions or fractionations. Of the plans, 84.5% were for paraspinal tumors, with 69, 302, and 170 in the cervical, thoracic, and lumbosacral spine, respectively. For each case, after contouring, a template plan using 9 intensity modulated RT fields based on disease site and tumor location was sent to ECHO through an application program interface plug-in from the treatment planning system. ECHO returned a plan that satisfied all critical structure hard constraints with optimal target volume coverage and the lowest achievable normal tissue doses. Upon ECHO completion, the planner received an e-mail indicating the plan was ready for review. The plan was accepted if all clinical criteria were met. Otherwise, a limited number of parameters could be adjusted for another ECHO run. Results The median planning target volume size was 84.3 cm3 (range, 6.9-633.2). The median time to produce 1 ECHO plan was 63.5 minutes (range, 11-340 minutes) and was largely dependent on the field sizes. Of the cases, 79.7% required 1 run to produce a clinically accepted plan, 13.3% required 1 additional run with minimal parameter adjustments, and 7.0% required ≥2 additional runs with significant parameter modifications. All plans met or bettered the institutional clinical criteria. Conclusions We successfully implemented automated stereotactic body RT paraspinal and other metastatic tumors planning. ECHO produced high-quality plans, improved planning efficiency and robustness, and enabled expedited treatment planning at our clinic.
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Affiliation(s)
- Linda Hong
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ying Zhou
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jie Yang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - James G Mechalakos
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Margie A Hunt
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Gig S Mageras
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jonathan Yang
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Josh Yamada
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Masoud Zarepisheh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
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van Haveren R, Breedveld S. Fast and exact Hessian computation for a class of nonlinear functions used in radiation therapy treatment planning. Phys Med Biol 2019; 64:16NT01. [PMID: 31039550 DOI: 10.1088/1361-6560/ab1e17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In nonlinear optimisation, using exact Hessian computations (full-Newton) hold superior convergence properties over quasi-Newton methods or gradient-based methods. However, for medium-scale problems, computing the Hessian can be computationally expensive and thus time-consuming. For solvers dedicated to a specific problem type, it can be advantageous to hard-code optimised implementations to keep the computation time to a minimum. In this paper we derive a computationally efficient canonical form for a class of additively and multiplicatively separable functions. The major computational cost is reduced to a single multiplication of the data matrix with itself, allowing simple parallellisation on modern-day multi-core processors. We present the approach in the practical application of radiation therapy treatment planning, where this form appears for many common functions. In this case, the data matrices are the dose-influence matrices. The method is compared against automatic differentiation.
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Affiliation(s)
- R van Haveren
- Author to whom any correspondence should be addressed
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Zarepisheh M, Hong L, Zhou Y, Oh JH, Mechalakos JG, Hunt MA, Mageras GS, Deasy JO. Automated intensity modulated treatment planning: The expedited constrained hierarchical optimization (ECHO) system. Med Phys 2019; 46:2944-2954. [PMID: 31055858 DOI: 10.1002/mp.13572] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 04/25/2019] [Accepted: 04/25/2019] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To develop and implement a fully automated approach to intensity modulated radiation therapy (IMRT) treatment planning. METHOD The optimization algorithm is developed based on a hierarchical constrained optimization technique and is referred internally at our institution as expedited constrained hierarchical optimization (ECHO). Beamlet contributions to regions-of-interest are precomputed and captured in the influence matrix. Planning goals are of two classes: hard constraints that are strictly enforced from the first step (e.g., maximum dose to spinal cord), and desirable goals that are sequentially introduced in three constrained optimization problems (better planning target volume (PTV) coverage, lower organ at risk (OAR) doses, and smoother fluence map). After solving the optimization problems using external commercial optimization engines, the optimal fluence map is imported into an FDA-approved treatment planning system (TPS) for leaf sequencing and accurate full dose calculation. The dose-discrepancy between the optimization and TPS dose calculation is then calculated and incorporated into optimization by a novel dose correction loop technique using Lagrange multipliers. The correction loop incorporates the leaf sequencing and scattering effects into optimization to improve the plan quality and reduce the calculation time. The resultant optimal fluence map is again imported into TPS for leaf sequencing and final dose calculation for plan evaluation and delivery. The workflow is automated using application program interface (API) scripting, requiring user interaction solely to prepare the contours and beam arrangement prior to launching the ECHO plug-in from the TPS. For each site, parameters and objective functions are chosen to represent clinical priorities. The first site chosen for clinical implementation was metastatic paraspinal lesions treated with stereotactic body radiotherapy (SBRT). As a first step, 75 ECHO paraspinal plans were generated retrospectively and compared with clinically treated plans generated by planners using VMAT (volumetric modulated arc therapy) with 4 to 6 partial arcs. Subsequently, clinical deployment began in April, 2017. RESULTS In retrospective study, ECHO plans were found to be dosimetrically superior with respect to tumor coverage, plan conformity, and OAR sparing. For example, the average PTV D95%, cord and esophagus max doses, and Paddick Conformity Index were improved, respectively, by 1%, 6%, 14%, and 15%, at a negligible 3% cost of the average skin D10cc dose. CONCLUSION Hierarchical constrained optimization is a powerful and flexible tool for automated IMRT treatment planning. The dosimetric correction step accurately accounts for detailed dosimetric multileaf collimator and scattering effects. The system produces high-quality, Pareto optimal plans and avoids the time-consuming trial-and-error planning process.
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Affiliation(s)
- Masoud Zarepisheh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Linda Hong
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ying Zhou
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - James G Mechalakos
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Margie A Hunt
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Gig S Mageras
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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12
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Wedenberg M, Beltran C, Mairani A, Alber M. Advanced Treatment Planning. Med Phys 2018; 45:e1011-e1023. [PMID: 30421811 DOI: 10.1002/mp.12943] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 03/22/2018] [Accepted: 04/22/2018] [Indexed: 12/15/2022] Open
Abstract
Treatment planning for protons and heavier ions is adapting technologies originally developed for photon dose optimization, but also has to meet its particular challenges. Since the quality of the applied dose is more sensitive to geometric uncertainties, treatment plan robust optimization has a much more prominent role in particle therapy. This has led to specific planning tools, approaches, and research into new formulations of the robust optimization problems. Tools for solution space navigation and automatic planning are also being adapted to particle therapy. These challenges become even greater when detailed models of relative biological effectiveness (RBE) are included into dose optimization, as is required for heavier ions.
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Affiliation(s)
| | - Chris Beltran
- Division of Medical Physics, Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA
| | - Andrea Mairani
- Heidelberg Ion Therapy Center (HIT), Heidelberg University Hospital, Heidelberg, Germany.,National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany.,The National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Markus Alber
- The National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy.,Section for Medical Physics, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
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Mai Y, Kong F, Yang Y, Zhou L, Li Y, Song T. Voxel-based automatic multi-criteria optimization for intensity modulated radiation therapy. Radiat Oncol 2018; 13:241. [PMID: 30518381 PMCID: PMC6280392 DOI: 10.1186/s13014-018-1179-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 11/09/2018] [Indexed: 11/10/2022] Open
Abstract
Background Automatic multi-criteria optimization is necessary for intensity modulated radiation therapy (IMRT) because of low planning efficiency and large plan quality uncertainty in current clinical practice. Most studies focused on imitating dosimetrists’ planning procedures to automate this process and ignored the fact that organ-based objective functions typically used in commercial treatment planning systems (such as dose-volume function) usually lead to sub-optimal plans. To guarantee the optimum results and to automate this process, we incorporate an improved automation strategy and a voxel-based optimization algorithm to generate a novel automatic multi-criteria optimization framework. We then evaluate it in clinical cases. Methods This novel automatic multi-criteria optimization framework incorporates a ranked priority-list based automatic constraints adjustment strategy and an in-house developed voxel-based optimization algorithm. Constraints are sequentially adjusted following a pre-defined priority list. Afterward, a voxel-based fluence map optimization (FMO) with an orientation to the newly updated constraints is launched to find a Pareto optimal solution. Loops of constraints adjustment are repeated until each of them could not be relaxed or tightened. The feasibility of the framework is evaluated with 10 automatic generated gynecology (GYN) cancer IMRT cases by comparing the dosimetric performance with the original. Results Plan quality improvement is observed for our automatic multi-criteria optimization method. Comparable DVHs are found for the planning target volume (PTV), but with better organs-at-risk (OAR) dose sparing. Among 13 evaluated dosimetric endpoints, 5 of them show significant improvements in automatically generated plans compared with the original plans. Investigation of improvement tendency during optimization exhibits gradual change as the optimization stage proceeds. An initial voxel-based optimization stage and in-low-priority dosimetric criteria tighten can significantly contribute to the optimization procedure. Conclusions We have successfully developed an automatic multi-criteria optimization framework that can dramatically reduce the current trial-and-error patterned planning workload while affording an efficient method to assure high plan quality consistency. This optimization framework is expected to greatly facilitate precise radiation therapy because of its advantages of planning efficiency and plan quality improvement.
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Affiliation(s)
- Yanhua Mai
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Fantu Kong
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Yiwei Yang
- Department of Radiation Oncology, Zhejiang Cancer Hospital, Zhejiang, 310022, Hangzhou, China
| | - Linghong Zhou
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Yongbao Li
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China.
| | - Ting Song
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
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Hussein M, Heijmen BJM, Verellen D, Nisbet A. Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations. Br J Radiol 2018; 91:20180270. [PMID: 30074813 DOI: 10.1259/bjr.20180270] [Citation(s) in RCA: 142] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Radiotherapy treatment planning of complex radiotherapy techniques, such as intensity modulated radiotherapy and volumetric modulated arc therapy, is a resource-intensive process requiring a high level of treatment planner intervention to ensure high plan quality. This can lead to variability in the quality of treatment plans and the efficiency in which plans are produced, depending on the skills and experience of the operator and available planning time. Within the last few years, there has been significant progress in the research and development of intensity modulated radiotherapy treatment planning approaches with automation support, with most commercial manufacturers now offering some form of solution. There is a rapidly growing number of research articles published in the scientific literature on the topic. This paper critically reviews the body of publications up to April 2018. The review describes the different types of automation algorithms, including the advantages and current limitations. Also included is a discussion on the potential issues with routine clinical implementation of such software, and highlights areas for future research.
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Affiliation(s)
- Mohammad Hussein
- 1 Metrology for Medical Physics Centre, National Physical Laboratory , Teddington , UK
| | - Ben J M Heijmen
- 2 Division of Medical Physics, Erasmus MC Cancer Institute , Rotterdam , The Netherlands
| | - Dirk Verellen
- 3 Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel (VUB) , Brussels , Belgium.,4 Radiotherapy Department, Iridium Kankernetwerk , Antwerp , Belgium
| | - Andrew Nisbet
- 5 Department of Medical Physics, Royal Surrey County Hospital NHS Foundation Trust , Guildford , UK.,6 Department of Physics, University of Surrey , Guildford , UK
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15
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Lin YH, Hong LX, Hunt MA, Berry SL. Use of a constrained hierarchical optimization dataset enhances knowledge-based planning as a quality assurance tool for prostate bed irradiation. Med Phys 2018; 45:4364-4369. [PMID: 30168160 DOI: 10.1002/mp.13163] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 05/15/2018] [Accepted: 08/21/2018] [Indexed: 11/10/2022] Open
Abstract
PURPOSE To investigate whether building a knowledge-based planning (KBP) model with prostate bed plans constructed from constrained hierarchical optimization (CHO) would result in more efficient model construction with more consistent output than a model built using plans from a traditional, trial-and-error-based optimization (TEO) technique. METHODS Three KBP models were constructed from plans from subsets of 58 post-prostatectomy patients treated with intensity-modulated radiation therapy. TEO54 was built from 54 TEO plans, selected to represent typical clinical variations in target and organ-at-risk sizes and shapes. CHO30 and TEO30 were built from the same 30 patients populated with CHO and TEO plans, respectively. The three models were each applied to a new set of 18 patient scans and dose-volume histogram estimates (DVHEs) were generated for rectal and bladder walls and compared for each patient. RESULTS CHO30 resulted in a significantly tighter range in DVHEs (P < 0.01) for both the rectal and bladder walls compared with either of the TEO models, indicating less uncertainty in the dose estimation. Plans resulting from KBP optimization using each model were very similar. CONCLUSION Populating a KBP model with CHO data resulted in a high quality model. Since CHO plans can be generated automatically offline in a process that necessitates little to no user interaction, a CHO-KBP model can quickly adapt to changes in plan evaluation criteria or planning techniques without the need to wait to accrue sufficient numbers of clinical TEO plans. This may facilitate the use of KBP approaches for initial or ongoing quality assurance procedures and plan quality audits.
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Affiliation(s)
- Yen Hwa Lin
- Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3, Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Linda X Hong
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Margie A Hunt
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Sean L Berry
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
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16
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麦 燕, 孔 繁, 杨 一, 李 永, 宋 婷, 周 凌. [Constraint priority list-based multi-objective optimization for intensity-modulated radiation therapy]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2018; 38:691-697. [PMID: 29997091 PMCID: PMC6765717 DOI: 10.3969/j.issn.1673-4254.2018.06.08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Indexed: 06/08/2023]
Abstract
In intensity-modulated radiation therapy (IMRT), it is time-consuming to repeatedly adjust the objectives manually to obtain the best tradeoff between the prescribed dose of the planning target volume and sparing the organs-at-risk. Here we propose a new method to realize automatic multi-objective IMRT optimization, which quantifies the clinical preferences into the constraint priority list and adjusts the dose constraints based on the list to obtain the optimal solutions under the dose constraints. This method contains automatic adjustment mechanism of the dose constraint and automatic voxel weighting factor-based FMO model. Every time the dose constraint is adjusted, the voxel weighting factor-based FMO model is launched to find a global optimal solution that satisfied the current constraints. We tested the feasibility and effectiveness of this method in 6 cases of cervical cancer with IMRT by comparing the original plan and the automatic optimization plan generated by this method. The results showed that with the same PTV coverage and uniformity, the automatic optimization plan had a better a dose sparing of the organs-at-risk and a better plan quality than the original plan, and resulted in obvious reductions of the average V45 of the rectum from (41.99∓13.31)% to (32.55∓22.27)% and of the bladder from (44.37∓4.08)% to (28.99∓15.25)%.
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Affiliation(s)
- 燕华 麦
- 南方医科大学生物医学工程学院,广东 广州 510515Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - 繁图 孔
- 南方医科大学生物医学工程学院,广东 广州 510515Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - 一威 杨
- 浙江省肿瘤医院放疗科,浙江 杭州 310022Department of Radiation Therapy, Zhejiang Provincial Cancer Hospital, Hangzhou 310022, China
| | - 永宝 李
- 中山大学肿瘤防治中心,广东 广州 510060Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - 婷 宋
- 南方医科大学生物医学工程学院,广东 广州 510515Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - 凌宏 周
- 南方医科大学生物医学工程学院,广东 广州 510515Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
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17
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Lin KM, Ehrgott M. Multiobjective navigation of external radiotherapy plans based on clinical criteria. JOURNAL OF MULTI-CRITERIA DECISION ANALYSIS 2018. [DOI: 10.1002/mcda.1628] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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18
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Kusters JMAM, Bzdusek K, Kumar P, van Kollenburg PGM, Kunze-Busch MC, Wendling M, Dijkema T, Kaanders JHAM. Automated IMRT planning in Pinnacle : A study in head-and-neck cancer. Strahlenther Onkol 2017; 193:1031-1038. [PMID: 28770294 DOI: 10.1007/s00066-017-1187-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Accepted: 07/07/2017] [Indexed: 01/16/2023]
Abstract
PURPOSE This study evaluates the performance and planning efficacy of the Auto-Planning (AP) module in the clinical version of Pinnacle 9.10 (Philips Radiation Oncology Systems, Fitchburg, WI, USA). METHODS AND MATERIALS Twenty automated intensity-modulated radiotherapy (IMRT) plans were compared with the original manually planned clinical IMRT plans from patients with oropharyngeal cancer. RESULTS Auto-Planning with IMRT offers similar coverage of the planning target volume as the original manually planned clinical plans, as well as better sparing of the contralateral parotid gland, contralateral submandibular gland, larynx, mandible, and brainstem. The mean dose of the contralateral parotid gland and contralateral submandibular gland could be reduced by 2.5 Gy and 1.7 Gy on average. The number of monitor units was reduced with an average of 143.9 (18%). Hands-on planning time was reduced from 1.5-3 h to less than 1 h. CONCLUSIONS The Auto-Planning module was able to produce clinically acceptable head and neck IMRT plans with consistent quality.
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Affiliation(s)
- J M A M Kusters
- Department of Radiation Oncology, Radboud university medical center, Postbox 9101, 6500 HB, Nijmegen, The Netherlands.
| | - K Bzdusek
- Philips Radiation Oncology Systems, Philips Healthcare, 53711, Fitchburg, WI, USA
| | - P Kumar
- Philips Innovation Campus, Philips Electronics India Ltd., Bangalore, India
| | - P G M van Kollenburg
- Department of Radiation Oncology, Radboud university medical center, Postbox 9101, 6500 HB, Nijmegen, The Netherlands
| | - M C Kunze-Busch
- Department of Radiation Oncology, Radboud university medical center, Postbox 9101, 6500 HB, Nijmegen, The Netherlands
| | - M Wendling
- Department of Radiation Oncology, Radboud university medical center, Postbox 9101, 6500 HB, Nijmegen, The Netherlands
| | - T Dijkema
- Department of Radiation Oncology, Radboud university medical center, Postbox 9101, 6500 HB, Nijmegen, The Netherlands
| | - J H A M Kaanders
- Department of Radiation Oncology, Radboud university medical center, Postbox 9101, 6500 HB, Nijmegen, The Netherlands
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van Haveren R, Ogryczak W, Verduijn GM, Keijzer M, Heijmen BJM, Breedveld S. Fast and fuzzy multi-objective radiotherapy treatment plan generation for head and neck cancer patients with the lexicographic reference point method (LRPM). Phys Med Biol 2017; 62:4318-4332. [PMID: 28475495 DOI: 10.1088/1361-6560/62/11/4318] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Previously, we have proposed Erasmus-iCycle, an algorithm for fully automated IMRT plan generation based on prioritised (lexicographic) multi-objective optimisation with the 2-phase ϵ-constraint (2pϵc) method. For each patient, the output of Erasmus-iCycle is a clinically favourable, Pareto optimal plan. The 2pϵc method uses a list of objective functions that are consecutively optimised, following a strict, user-defined prioritisation. The novel lexicographic reference point method (LRPM) is capable of solving multi-objective problems in a single optimisation, using a fuzzy prioritisation of the objectives. Trade-offs are made globally, aiming for large favourable gains for lower prioritised objectives at the cost of only slight degradations for higher prioritised objectives, or vice versa. In this study, the LRPM is validated for 15 head and neck cancer patients receiving bilateral neck irradiation. The generated plans using the LRPM are compared with the plans resulting from the 2pϵc method. Both methods were capable of automatically generating clinically relevant treatment plans for all patients. For some patients, the LRPM allowed large favourable gains in some treatment plan objectives at the cost of only small degradations for the others. Moreover, because of the applied single optimisation instead of multiple optimisations, the LRPM reduced the average computation time from 209.2 to 9.5 min, a speed-up factor of 22 relative to the 2pϵc method.
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Affiliation(s)
- Rens van Haveren
- Department of Radiation Oncology, Erasmus MC-Cancer Institute, PO Box 2040, 3000 CA Rotterdam, The Netherlands
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Unkelbach J, Botas P, Giantsoudi D, Gorissen BL, Paganetti H. Reoptimization of Intensity Modulated Proton Therapy Plans Based on Linear Energy Transfer. Int J Radiat Oncol Biol Phys 2016; 96:1097-1106. [PMID: 27869082 PMCID: PMC5133459 DOI: 10.1016/j.ijrobp.2016.08.038] [Citation(s) in RCA: 135] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 08/19/2016] [Accepted: 08/25/2016] [Indexed: 01/20/2023]
Abstract
PURPOSE We describe a treatment plan optimization method for intensity modulated proton therapy (IMPT) that avoids high values of linear energy transfer (LET) in critical structures located within or near the target volume while limiting degradation of the best possible physical dose distribution. METHODS AND MATERIALS To allow fast optimization based on dose and LET, a GPU-based Monte Carlo code was extended to provide dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dose objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of LET and physical dose (LET×D). To first approximation, LET×D represents a measure of the additional biological dose that is caused by high LET. RESULTS The method is effective for treatments where serial critical structures with maximum dose constraints are located within or near the target. We report on 5 patients with intracranial tumors (high-grade meningiomas, base-of-skull chordomas, ependymomas) in whom the target volume overlaps with the brainstem and optic structures. In all cases, high LET×D in critical structures could be avoided while minimally compromising physical dose planning objectives. CONCLUSION LET-based reoptimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose-based and relative biological effectiveness (RBE)-based planning. The method makes IMPT treatments safer by mitigating a potentially increased risk of side effects resulting from elevated RBE of proton beams near the end of range.
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Affiliation(s)
- Jan Unkelbach
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts.
| | - Pablo Botas
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts; Faculty of Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany
| | - Drosoula Giantsoudi
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Bram L Gorissen
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts
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21
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Müller BS, Wilkens JJ. Prioritized efficiency optimization for intensity modulated proton therapy. Phys Med Biol 2016; 61:8249-8265. [DOI: 10.1088/0031-9155/61/23/8249] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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22
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Berry SL, Boczkowski A, Ma R, Mechalakos J, Hunt M. Interobserver variability in radiation therapy plan output: Results of a single-institution study. Pract Radiat Oncol 2016; 6:442-449. [PMID: 27374191 DOI: 10.1016/j.prro.2016.04.005] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 04/11/2016] [Accepted: 04/27/2016] [Indexed: 12/21/2022]
Abstract
PURPOSE We investigated the sources of variability in radiation therapy treatment plan output between planners within a single institution. METHODS AND MATERIALS Forty treatment planners across 5 campuses of an institution created a plan on the same thoracic esophagus patient computed tomography scan and structure set. Plans were scored and ranked based on the planner's adherence to an ordered list of target dose coverage and normal tissue evaluation criteria. A runs test was used to identify whether any of the studied planner qualities influenced the ranking. Spearman rank correlation was used to investigate whether plan score correlated with years of experience or planned monitor units. RESULTS The distribution of scores, ranging from 80.24 to 135.89, was negatively skewed (mean, 128.7; median, 131.5). No statistically significant relationship between plan score and campus (P = .193), job title (P = .174), previous outside experience (P = .611), or number of gantry angles (P = .156) was discovered. No statistical correlation between plan score and monitor unit or years of experience was found. CONCLUSIONS Despite clear and established critical organ dose criteria and well-documented planning guidelines, planning variation still occurs, even among members of the same institution. Because plan consistency does not seem to significantly correlate with experience, career path, or campus, investigation into alternate methods beyond additional education and training to reduce this variation, such as knowledge-based planning or advanced optimization techniques, is necessary.
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Affiliation(s)
- Sean L Berry
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Amanda Boczkowski
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Rongtao Ma
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - James Mechalakos
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Margie Hunt
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
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Baumann M, Krause M, Overgaard J, Debus J, Bentzen SM, Daartz J, Richter C, Zips D, Bortfeld T. Radiation oncology in the era of precision medicine. Nat Rev Cancer 2016; 16:234-49. [PMID: 27009394 DOI: 10.1038/nrc.2016.18] [Citation(s) in RCA: 514] [Impact Index Per Article: 64.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Technological advances and clinical research over the past few decades have given radiation oncologists the capability to personalize treatments for accurate delivery of radiation dose based on clinical parameters and anatomical information. Eradication of gross and microscopic tumours with preservation of health-related quality of life can be achieved in many patients. Two major strategies, acting synergistically, will enable further widening of the therapeutic window of radiation oncology in the era of precision medicine: technology-driven improvement of treatment conformity, including advanced image guidance and particle therapy, and novel biological concepts for personalized treatment, including biomarker-guided prescription, combined treatment modalities and adaptation of treatment during its course.
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Affiliation(s)
- Michael Baumann
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay - National Center for Radiation Research in Oncology (NCRO), Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, and Helmholtz-Zentrum Dresden-Rossendorf, Fetscherstrasse 74, 01307 Dresden
- National Center for Tumor Diseases (NCT), Fetscherstrasse 74, 01307 Dresden
- German Cancer Consortium (DKTK) Dresden, Germany
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiation Oncology, Bautzner Landstrasse 400, 01328 Dresden, Germany
| | - Mechthild Krause
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay - National Center for Radiation Research in Oncology (NCRO), Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, and Helmholtz-Zentrum Dresden-Rossendorf, Fetscherstrasse 74, 01307 Dresden
- National Center for Tumor Diseases (NCT), Fetscherstrasse 74, 01307 Dresden
- German Cancer Consortium (DKTK) Dresden, Germany
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiation Oncology, Bautzner Landstrasse 400, 01328 Dresden, Germany
| | - Jens Overgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Nørrebrogade 44, 8000 Aarhus C, Denmark
| | - Jürgen Debus
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), University of Heidelberg Medical School and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, 69120 Heidelberg
- Heidelberg Ion Therapy Center (HIT), Department of Radiation Oncology, University of Heidelberg Medical School, Im Neuenheimer Feld 400, 69120 Heidelberg
- German Cancer Consortium (DKTK) Heidelberg, Germany
| | - Søren M Bentzen
- Department of Epidemiology and Public Health and Greenebaum Cancer Center, University of Maryland School of Medicine, 22 S Greene Street S9a03, Baltimore, Maryland 21201, USA
| | - Juliane Daartz
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital and Harvard Medical School, 1000 Blossom Street Cox 362, Boston, Massachusetts 02114, USA
| | - Christian Richter
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay - National Center for Radiation Research in Oncology (NCRO), Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, and Helmholtz-Zentrum Dresden-Rossendorf, Fetscherstrasse 74, 01307 Dresden
- National Center for Tumor Diseases (NCT), Fetscherstrasse 74, 01307 Dresden
- German Cancer Consortium (DKTK) Dresden, Germany
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Daniel Zips
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- German Cancer Consortium Tübingen, Postfach 2669, 72016 Tübingen
- Department of Radiation Oncology, Faculty of Medicine and University Hospital Tübingen, Eberhard Karls Universität Tübingen, Hoppe-Seyler-Strasse 3, 72016 Tübingen, Germany
| | - Thomas Bortfeld
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital and Harvard Medical School, 1000 Blossom Street Cox 362, Boston, Massachusetts 02114, USA
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Zarepisheh M, Uribe-Sanchez AF, Li N, Jia X, Jiang SB. A multicriteria framework with voxel-dependent parameters for radiotherapy treatment plan optimization. Med Phys 2014; 41:041705. [PMID: 24694125 DOI: 10.1118/1.4866886] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To establish a new mathematical framework for radiotherapy treatment optimization with voxel-dependent optimization parameters. METHODS In the treatment plan optimization problem for radiotherapy, a clinically acceptable plan is usually generated by an optimization process with weighting factors or reference doses adjusted for a set of the objective functions associated to the organs. Recent discoveries indicate that adjusting parameters associated with each voxel may lead to better plan quality. However, it is still unclear regarding the mathematical reasons behind it. Furthermore, questions about the objective function selection and parameter adjustment to assure Pareto optimality as well as the relationship between the optimal solutions obtained from the organ-based and voxel-based models remain unanswered. To answer these questions, the authors establish in this work a new mathematical framework equipped with two theorems. RESULTS The new framework clarifies the different consequences of adjusting organ-dependent and voxel-dependent parameters for the treatment plan optimization of radiation therapy, as well as the impact of using different objective functions on plan qualities and Pareto surfaces. The main discoveries are threefold: (1) While in the organ-based model the selection of the objective function has an impact on the quality of the optimized plans, this is no longer an issue for the voxel-based model since the Pareto surface is independent of the objective function selection and the entire Pareto surface could be generated as long as the objective function satisfies certain mathematical conditions; (2) All Pareto solutions generated by the organ-based model with different objective functions are parts of a unique Pareto surface generated by the voxel-based model with any appropriate objective function; (3) A much larger Pareto surface is explored by adjusting voxel-dependent parameters than by adjusting organ-dependent parameters, possibly allowing for the generation of plans with better trade-offs among different clinical objectives. CONCLUSIONS The authors have developed a mathematical framework for radiotherapy treatment optimization using voxel-based parameters. The authors can improve the plan quality by adjusting voxel-based weighting factors and exploring the unique and large Pareto surface which include all the Pareto surfaces that can be generated by organ-based model using different objective functions.
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Affiliation(s)
- Masoud Zarepisheh
- Center for Advanced Radiotherapy Technologies and Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92037-0843
| | - Andres F Uribe-Sanchez
- Center for Advanced Radiotherapy Technologies and Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92037-0843
| | - Nan Li
- Center for Advanced Radiotherapy Technologies and Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92037-0843
| | - Xun Jia
- Center for Advanced Radiotherapy Technologies and Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92037-0843
| | - Steve B Jiang
- Center for Advanced Radiotherapy Technologies and Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92037-0843
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Kalantzis G, Apte A. A novel reduced-order prioritized optimization method for radiation therapy treatment planning. IEEE Trans Biomed Eng 2014; 61:1062-70. [PMID: 24658231 DOI: 10.1109/tbme.2013.2293779] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this study, a novel reduced order prioritized algorithm is presented for optimization in radiation therapy treatment planning. The proposed method consists of three stages. In the first stage, the intensity space was sampled by solving a series of unconstrained optimization problems. The objective function of the first stage is expressed as a scalarized weighted sum of partial objectives for the target and organ at risk. Latin hypercube sampling was utilized to define the weights for each run of the unconstrained optimizations. In the second stage, principal component analysis is applied to the solutions determined in the first stage to identify the major eigen modes in the intensities space, significantly reducing the number of independent variables. In the third stage, treatment planning goals/objectives are prioritized, and the problem is solved in the reduced order space. After each objective is optimized, that objective function is converted into a constraint for the lower-priority objectives. In the current formulation, a slip factor is used to relax the hard constraints for planning target volume (PTV) coverage. The applicability of the proposed method is demonstrated for one prostate and one lung intensity-modulated radiation therapy treatment plan. Upon completion of the sequential prioritized optimization, the mean dose at the rectum and bladder was reduced by 21.3% and 22.4%, respectively. Additionally, we investigated the effect of the slip factor 's' on PTV coverage and we found minimal degradation of the tumor dose (∼4%). Finally, the speed up factors upon the dimensionality reduction were as high as 49.9 without compromising the quality of the results.
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Schlaefer A, Viulet T, Muacevic A, Fürweger C. Multicriteria optimization of the spatial dose distribution. Med Phys 2014; 40:121720. [PMID: 24320506 DOI: 10.1118/1.4828840] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Treatment planning for radiation therapy involves trade-offs with respect to different clinical goals. Typically, the dose distribution is evaluated based on few statistics and dose-volume histograms. Particularly for stereotactic treatments, the spatial dose distribution represents further criteria, e.g., when considering the gradient between subregions of volumes of interest. The authors have studied how to consider the spatial dose distribution using a multicriteria optimization approach. METHODS The authors have extended a stepwise multicriteria optimization approach to include criteria with respect to the local dose distribution. Based on a three-dimensional visualization of the dose the authors use a software tool allowing interaction with the dose distribution to map objectives with respect to its shape to a constrained optimization problem. Similarly, conflicting criteria are highlighted and the planner decides if and where to relax the shape of the dose distribution. RESULTS To demonstrate the potential of spatial multicriteria optimization, the tool was applied to a prostate and meningioma case. For the prostate case, local sparing of the rectal wall and shaping of a boost volume are achieved through local relaxations and while maintaining the remaining dose distribution. For the meningioma, target coverage is improved by compromising low dose conformality toward noncritical structures. A comparison of dose-volume histograms illustrates the importance of spatial information for achieving the trade-offs. CONCLUSIONS The results show that it is possible to consider the location of conflicting criteria during treatment planning. Particularly, it is possible to conserve already achieved goals with respect to the dose distribution, to visualize potential trade-offs, and to relax constraints locally. Hence, the proposed approach facilitates a systematic exploration of the optimal shape of the dose distribution.
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Affiliation(s)
- Alexander Schlaefer
- Medical Robotics Group, Universität zu Lübeck, Lübeck 23562, Germany and Institute of Medical Technology, Hamburg University of Technology, Hamburg 21073, Germany
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Lin KM, Simpson J, Sasso G, Raith A, Ehrgott M. Quality assessment for VMAT prostate radiotherapy planning based on data envelopment analysis. Phys Med Biol 2013; 58:5753-69. [PMID: 23912157 DOI: 10.1088/0031-9155/58/16/5753] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The majority of commercial radiotherapy treatment planning systems requires planners to iteratively adjust the plan parameters in order to find a satisfactory plan. This iterative trial-and-error nature of radiotherapy treatment planning results in an inefficient planning process and in order to reduce such inefficiency, plans can be accepted without achieving the best attainable quality. We propose a quality assessment method based on data envelopment analysis (DEA) to address this inefficiency. This method compares a plan of interest to a set of past delivered plans and searches for evidence of potential further improvement. With the assistance of DEA, planners will be able to make informed decisions on whether further planning is required and ensure that a plan is only accepted when the plan quality is close to the best attainable one. We apply the DEA method to 37 prostate plans using two assessment parameters: rectal generalized equivalent uniform dose (gEUD) as the input and D95 (the minimum dose that is received by 95% volume of a structure) of the planning target volume (PTV) as the output. The percentage volume of rectum overlapping PTV is used to account for anatomical variations between patients and is included in the model as a non-discretionary output variable. Five plans that are considered of lesser quality by DEA are re-optimized with the goal to further improve rectal sparing. After re-optimization, all five plans improve in rectal gEUD without clinically considerable deterioration of the PTV D95 value. For the five re-optimized plans, the rectal gEUD is reduced by an average of 1.84 Gray (Gy) with only an average reduction of 0.07 Gy in PTV D95. The results demonstrate that DEA can correctly identify plans with potential improvements in terms of the chosen input and outputs.
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Affiliation(s)
- Kuan-Min Lin
- Department of Engineering Science, University of Auckland, 70 Symonds Street, Auckland, New Zealand.
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Salari E, Unkelbach J. A column-generation-based method for multi-criteria direct aperture optimization. Phys Med Biol 2013; 58:621-39. [PMID: 23318527 DOI: 10.1088/0031-9155/58/3/621] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Navigation-based multi-criteria optimization has been introduced to radiotherapy planning in order to allow the interactive exploration of trade-offs between conflicting clinical goals. However, this has been mainly applied to fluence map optimization. The subsequent leaf sequencing step may cause dose discrepancy, leading to human iteration loops in the treatment planning process that multi-criteria methods were meant to avoid. To circumvent this issue, this paper investigates the application of direct aperture optimization methods in the context of multi-criteria optimization. We develop a solution method to directly obtain a collection of apertures that can adequately span the entire Pareto surface. To that end, we extend the column generation method for direct aperture optimization to a multi-criteria setting in which apertures that can improve the entire Pareto surface are sequentially identified and added to the treatment plan. Our proposed solution method can be embedded in a navigation-based multi-criteria optimization framework, in which the treatment planner explores the trade-off between treatment objectives directly in the space of deliverable apertures. Our solution method is demonstrated for a paraspinal case where the trade-off between target coverage and spinal-cord sparing is studied. The computational results validate that our proposed method obtains a balanced approximation of the Pareto surface over a wide range of clinically relevant plans.
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Affiliation(s)
- Ehsan Salari
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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Xhaferllari I, Wong E, Bzdusek K, Lock M, Chen J. Automated IMRT planning with regional optimization using planning scripts. J Appl Clin Med Phys 2013; 14:4052. [PMID: 23318393 PMCID: PMC5714048 DOI: 10.1120/jacmp.v14i1.4052] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2012] [Revised: 08/10/2012] [Accepted: 09/04/2012] [Indexed: 12/01/2022] Open
Abstract
Intensity‐modulated radiation therapy (IMRT) has become a standard technique in radiation therapy for treating different types of cancers. Various class solutions have been developed for simple cases (e.g., localized prostate, whole breast) to generate IMRT plans efficiently. However, for more complex cases (e.g., head and neck, pelvic nodes), it can be time‐consuming for a planner to generate optimized IMRT plans. To generate optimal plans in these more complex cases which generally have multiple target volumes and organs at risk, it is often required to have additional IMRT optimization structures such as dose limiting ring structures, adjust beam geometry, select inverse planning objectives and associated weights, and additional IMRT objectives to reduce cold and hot spots in the dose distribution. These parameters are generally manually adjusted with a repeated trial and error approach during the optimization process. To improve IMRT planning efficiency in these more complex cases, an iterative method that incorporates some of these adjustment processes automatically in a planning script is designed, implemented, and validated. In particular, regional optimization has been implemented in an iterative way to reduce various hot or cold spots during the optimization process that begins with defining and automatic segmentation of hot and cold spots, introducing new objectives and their relative weights into inverse planning, and turn this into an iterative process with termination criteria. The method has been applied to three clinical sites: prostate with pelvic nodes, head and neck, and anal canal cancers, and has shown to reduce IMRT planning time significantly for clinical applications with improved plan quality. The IMRT planning scripts have been used for more than 500 clinical cases. PACS numbers: 87.55.D, 87.55.de
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Affiliation(s)
- Ilma Xhaferllari
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada.
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Craft D, Richter C. Deliverable navigation for multicriteria step and shoot IMRT treatment planning. Phys Med Biol 2012; 58:87-103. [PMID: 23221364 DOI: 10.1088/0031-9155/58/1/87] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
We consider Pareto surface based multi-criteria optimization for step and shoot IMRT planning. By analyzing two navigation algorithms, we show both theoretically and in practice that the number of plans needed to form convex combinations of plans during navigation can be kept small (much less than the theoretical maximum number needed in general, which is equal to the number of objectives for on-surface Pareto navigation). Therefore a workable approach for directly deliverable navigation in this setting is to segment the underlying Pareto surface plans and then enforce the mild restriction that only a small number of these plans are active at any time during plan navigation, thus limiting the total number of segments used in the final plan.
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Affiliation(s)
- David Craft
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA.
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Fredriksson A. Automated improvement of radiation therapy treatment plans by optimization under reference dose constraints. Phys Med Biol 2012; 57:7799-811. [DOI: 10.1088/0031-9155/57/23/7799] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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JAGANNATHAN RUPA, PETROVIC SANJA, MCKENNA ANGELA, NEWTON LOUISE. A NOVEL TWO PHASE RETRIEVAL MECHANISM FOR A CLINICAL CASE BASED REASONING SYSTEM FOR RADIOTHERAPY TREATMENT PLANNING. INT J ARTIF INTELL T 2012. [DOI: 10.1142/s0218213012400179] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper presents a decision support system for radiotherapy treatment planning for head, neck and brain cancer. The aim of a treatment plan is to apply radiation to kill tumor cells, while minimizing the damage to healthy tissue and critical organs. Since treatment planning is a complex decision making process that relies heavily on the subjective experience of clinicians, we propose the use of case-based reasoning (CBR), in which problems are solved based on the solutions of similar past problems. This paper focuses on the case retrieval process of a CBR system. The attributes, which describe the cases, are selected by assessing their effect on the performance of the CBR system. We have developed a context sensitive local weighting scheme that assigns weights to attributes based on their value and the values of other attributes in the target case. A novel two phase retrieval mechanism is developed, in which each phase is optimized to retrieve a particular part of the solution. We also present an original use of fuzzy logic in order to represent nonlinearity in the similarity measure. Experiments, which evaluate the similarity measure using real brain cancer patient cases, show promising results.
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Affiliation(s)
- RUPA JAGANNATHAN
- Automated Scheduling, Optimisation and Planning Research Group, School of Computer Science, University of Nottingham, Nottingham, UK
| | - SANJA PETROVIC
- Automated Scheduling, Optimisation and Planning Research Group, School of Computer Science, University of Nottingham, Nottingham, UK
| | - ANGELA MCKENNA
- Department of Medical Physics, Nottingham University Hospitals NHS Trust Nottingham, UK
| | - LOUISE NEWTON
- Department of Medical Physics, Nottingham University Hospitals NHS Trust Nottingham, UK
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Long T, Matuszak M, Feng M, Fraass BA, Ten Haken RK, Romeijn HE. Sensitivity analysis for lexicographic ordering in radiation therapy treatment planning. Med Phys 2012; 39:3445-55. [PMID: 22755724 DOI: 10.1118/1.4720218] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To introduce a method to efficiently identify and calculate meaningful tradeoffs between criteria in an interactive IMRT treatment planning procedure. The method provides a systematic approach to developing high-quality radiation therapy treatment plans. METHODS Treatment planners consider numerous dosimetric criteria of varying importance that, when optimized simultaneously through multicriteria optimization, yield a Pareto frontier which represents the set of Pareto-optimal treatment plans. However, generating and navigating this frontier is a time-consuming, nontrivial process. A lexicographic ordering (LO) approach to IMRT uses a physician's criteria preferences to partition the treatment planning decisions into a multistage treatment planning model. Because the relative importance of criteria optimized in the different stages may not necessarily constitute a strict prioritization, the authors introduce an interactive process, sensitivity analysis in lexicographic ordering (SALO), to allow the treatment planner control over the relative sequential-stage tradeoffs. By allowing this flexibility within a structured process, SALO implicitly restricts attention to and allows exploration of a subset of the Pareto efficient frontier that the physicians have deemed most important. RESULTS Improvements to treatment plans over a LO approach were found by implementing the SALO procedure on a brain case and a prostate case. In each stage, a physician assessed the tradeoff between previous stage and current stage criteria. The SALO method provided critical tradeoff information through curves approximating the relationship between criteria, which allowed the physician to determine the most desirable treatment plan. CONCLUSIONS The SALO procedure provides treatment planners with a directed, systematic process to treatment plan selection. By following a physician's prioritization, the treatment planner can avoid wasting effort considering clinically inferior treatment plans. The planner is guided by criteria importance, but given the information necessary to accurately adjust the relative importance at each stage. Through these attributes, the SALO procedure delivers an approach well balanced between efficiency and flexibility.
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Affiliation(s)
- T Long
- Department of Industrial and Operations Engineering, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI 48109-2117, USA
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Prioritized optimization in intensity modulated proton therapy. Z Med Phys 2012; 22:21-8. [DOI: 10.1016/j.zemedi.2011.05.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2011] [Revised: 05/06/2011] [Accepted: 05/11/2011] [Indexed: 11/20/2022]
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Bortfeld T, Jeraj R. The physical basis and future of radiation therapy. Br J Radiol 2011; 84:485-98. [PMID: 21606068 PMCID: PMC3473639 DOI: 10.1259/bjr/86221320] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2010] [Revised: 12/23/2010] [Accepted: 01/06/2011] [Indexed: 12/25/2022] Open
Abstract
The remarkable progress in radiation therapy over the last century has been largely due to our ability to more effectively focus and deliver radiation to the tumour target volume. Physics discoveries and technology inventions have been an important driving force behind this progress. However, there is still plenty of room left for future improvements through physics, for example image guidance and four-dimensional motion management and particle therapy, as well as increased efficiency of more compact and cheaper technologies. Bigger challenges lie ahead of physicists in radiation therapy beyond the dose localisation problem, for example in the areas of biological target definition, improved modelling for normal tissues and tumours, advanced multicriteria and robust optimisation, and continuous incorporation of advanced technologies such as molecular imaging. The success of physics in radiation therapy has been based on the continued "fuelling" of the field with new discoveries and inventions from physics research. A key to the success has been the application of the rigorous scientific method. In spite of the importance of physics research for radiation therapy, too few physicists are currently involved in cutting-edge research. The increased emphasis on more "professionalism" in medical physics will tip the situation even more off balance. To prevent this from happening, we argue that medical physics needs more research positions, and more and better academic programmes. Only with more emphasis on medical physics research will the future of radiation therapy and other physics-related medical specialties look as bright as the past, and medical physics will maintain a status as one of the most exciting fields of applied physics.
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Affiliation(s)
- T Bortfeld
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, 30 Fruit St., Boston, MA 02114, USA.
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Lougovski P, LeNoach J, Zhu L, Ma Y, Censor Y, Xing L. Toward truly optimal IMRT dose distribution: inverse planning with voxel-specific penalty. Technol Cancer Res Treat 2011; 9:629-36. [PMID: 21070085 DOI: 10.1177/153303461000900611] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
PURPOSE To establish an inverse planning framework with adjustable voxel penalty for more conformal IMRT dose distribution as well as improved interactive controllability over the regional dose distribution of the resultant plan. MATERIALS AND METHOD In the proposed coarse-to-fine planning scheme, a conventional inverse planning with organ specific parameters is first performed. The voxel penalty scheme is then "switched on" by allowing the prescription dose to change on an individual voxel scale according to the deviation of the actual voxel dose from the ideally desired dose. The rationale here is intuitive: when the dose at a voxel does not meet its ideal dose, it simply implies that this voxel is not competitive enough when compared with the ones that have met their planning goal. In this case, increasing the penalty of the voxel by varying the prescription can boost its competitiveness and thus improve its dose. After the prescription adjustment, the plan is re-optimized. The dose adjustment/re-optimization procedure is repeated until the resultant dose distribution cannot be improved anymore. The prescription adjustment on a finer scale can be accomplished either automatically or manually. In the latter case, the regions/voxels where a dose improvement is needed are selected visually, unlike in the automatic case where the selection is done purely based on the difference of the actual dose at a given voxel and its ideal prescription. The performance of the proposed method is evaluated using a head and neck and a prostate case. RESULTS An inverse planning framework with the voxel-specific penalty is established. By adjusting voxel prescriptions iteratively to boost the region where large mismatch between the actual calculated and desired doses occurs, substantial improvements can be achieved in the final dose distribution. The proposed method is applied to a head and neck case and a prostate case. For the former case, a significant reduction in the maximum dose to the brainstem is achieved while the PTV dose coverage is greatly improved. The doses to other organs at risk are also reduced, ranging from 10% to 30%. For the prostate case, the use of the voxel penalty scheme also results in vast improvements to the final dose distribution. The PTV experiences improved dose uniformity and the mean dose to the rectum and bladder is reduced by as much as 15%. CONCLUSION Introduction of the spatially non-uniform and adjustable prescription provides room for further improvements of currently achievable dose distributions and equips the planner with an effective tool to modify IMRT dose distributions interactively. The technique is easily implementable in any existing inverse planning platform, which should facilitate clinical IMRT planning process and, in future, off-line/on-line adaptive IMRT.
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Affiliation(s)
- Pavel Lougovski
- Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Stanford, CA 94305-5847
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Jensen AD, Nill S, Rochet N, Bendl R, Harms W, Huber PE, Debus J, Münter MW. Whole-abdominal IMRT for advanced ovarian carcinoma: planning issues and feasibility. Phys Med 2011; 27:194-202. [PMID: 21215671 DOI: 10.1016/j.ejmp.2010.12.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2010] [Revised: 11/09/2010] [Accepted: 12/09/2010] [Indexed: 11/15/2022] Open
Abstract
INTRODUCTION Despite enormous efforts to improve therapeutic strategies for patients with advanced ovarian carcinoma, outcome remains poor even with the advent cisplatinum-based chemotherapy regimen or taxanes with over 70% of patients developing local failure. Several trials were able to establish the potential benefit of adjuvant whole abdominal RT (WAI) though at the cost of sometimes marked side-effects. New technologies like IMRT have the potential of sparing normal tissues thus also potentially limiting treatment-related toxicity, hence a phase I trial was initiated to evaluate potential clinical benefit of WAI with IMRT. We intended to demonstrate that whole-abdominal IMRT is feasible and can be used in a routine clinical setting. METHODS A water-equivalent phantom containing OARs was created simulating organ shape of the upper abdomen to investigate the necessary number of beams for the upper abdominal target irrespective of the number of segments and hence treatment times. We prescribed a total dose of 30 Gy in 1.5 Gy fractions to the median of the target. IMRT treatment plans for three patients with advanced ovarian cancer were created using 2 isocentres and between 12 and 14 beams while restricting the number of segments so as to restrict treatment times to less than 45 min. Dose to OARs such as kidneys and liver was strictly limited even below established maxima. RESULTS In the phantom plans, no clear indication as to the optimum number of beams could be shown though there seems to be a slight trend toward a higher number of beams yielding better results. Examples demonstrating clinically inacceptable dose distributions for plans using only 9 beams. Acceptable treatment plans for real patients could be achieved using 12-14 beams and 2 isocentres. Treatment plans consisted of 264-286 segments resulting in an overall treatment time of approximately 37-45 min. Mean doses to the kidneys could be limited to 29.3% [23.1-33.2%] (right), and 26.8% [21-30.4%] (left). 50% of the liver received less than 72.4% [61-83%]. CONCLUSION IMRT for whole abdominal irradiation in patients with advanced ovarian carcinoma is applicable and feasible though treatment planning is complex and time-consuming. There is a significant reduction of dose to critical organs by using IMRT while maintaining target volume coverage.
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Affiliation(s)
- A D Jensen
- Dept. of Radiation Oncology, University of Heidelberg Medical School, Germany.
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Stock M, Dörr W, Stromberger C, Mock U, Koizar S, Pötter R, Georg D. Investigations on parotid gland recovery after IMRT in head and neck tumor patients. Strahlenther Onkol 2010; 186:665-71. [PMID: 21136030 DOI: 10.1007/s00066-010-2157-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2010] [Accepted: 09/16/2010] [Indexed: 11/30/2022]
Abstract
PURPOSE in recent years, the role of intensity-modulated radiotherapy (IMRT) for head and neck irradiation has increased. The main motivation is sparing the parotid gland and reduction of xerostomia. Generally, relative parotid volumes have been evaluated for treatment outcome and planning constraints, neglecting that absolute parotid volumes can vary significantly. The aim of the present study was to investigate changes in parotid gland function and set this in relation to absolute volumes. MATERIAL AND METHODS 46 head and neck patients were treated by sparing at least the contralateral parotid gland. The mean dose to the contralateral gland was limited to 26 Gy. Parotid function was measured with scintigraphy before and at 3, 6, 9, and 12 months after radiotherapy. Gland recovery was correlated with absolute parotid gland volumes and mean dose. Finally the dose-effect relationship was investigated. RESULTS the dose-volume histograms (DVHs) for the ipsi- and contralateral glands were significantly different. A correlation between absolute volumes receiving certain doses and the function loss after 3, 6, 9, and 12 months was found. The most significant correlation was found for the absolute volume that received at least 40 Gy (aV40). ED50 values of 23-38 Gy were observed for more than 50% function loss and and 52-68 Gy afor more than 75% function loss. CONCLUSION the mean dose, aV40 or aV26, revealed similar correlations with the excretion rate and with recovery. Hence, also absolute volumes can be used for treatment planning. Longer recovery times show higher ED50 values indicating partial regeneration of gland functions.
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Affiliation(s)
- Markus Stock
- Department of Radiotherapy, Medical University Vienna, Vienna, Austria.
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Schell S, Wilkens JJ. Advanced treatment planning methods for efficient radiation therapy with laser accelerated proton and ion beams. Med Phys 2010; 37:5330-40. [PMID: 21089768 DOI: 10.1118/1.3491406] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Stefan Schell
- Department of Radiation Oncology, Technische Universität München, Klinikum Rechts der Isar, Ismaninger Str 22, 81675 München, Germany.
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Breedveld S, Storchi PRM, Heijmen BJM. The equivalence of multi-criteria methods for radiotherapy plan optimization. Phys Med Biol 2009; 54:7199-209. [PMID: 19920305 DOI: 10.1088/0031-9155/54/23/011] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Several methods can be used to achieve multi-criteria optimization of radiation therapy treatment planning, which strive for Pareto-optimality. The property of the solution being Pareto optimal is desired, because it guarantees that no criteria can be improved without deteriorating another criteria. The most widely used methods are the weighted-sum method, in which the different treatment objectives are weighted, and constrained optimization methods, in which treatment goals are set and the algorithm has to find the best plan fulfilling these goals. The constrained method used in this paper, the 2p element of c (2-phase element-constraint) method is based on the element-constraint method, which generates Pareto-optimal solutions. Both approaches are uniquely related to each other. In this paper, we will show that it is possible to switch from the constrained method to the weighted-sum method by using the Lagrange multipliers from the constrained optimization problem, and vice versa by setting the appropriate constraints. In general, the theory presented in this paper can be useful in cases where a new situation is slightly different from the original situation, e.g. in online treatment planning, with deformations of the volumes of interest, or in automated treatment planning, where changes to the automated plan have to be made. An example of the latter is given where the planner is not satisfied with the result from the constrained method and wishes to decrease the dose in a structure. By using the Lagrange multipliers, a weighted-sum optimization problem is constructed, which generates a Pareto-optimal solution in the neighbourhood of the original plan, but fulfills the new treatment objectives.
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Affiliation(s)
- Sebastiaan Breedveld
- Department of Radiation Oncology, Erasmus MC Rotterdam, Groene Hilledijk 301, 3075 EA Rotterdam, The Netherlands.
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Stieler F, Yan H, Lohr F, Wenz F, Yin FF. Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning. Radiat Oncol 2009; 4:39. [PMID: 19781059 PMCID: PMC2760562 DOI: 10.1186/1748-717x-4-39] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2009] [Accepted: 09/25/2009] [Indexed: 11/10/2022] Open
Abstract
Background Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT) is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI) guided system was developed and examined. Methods The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS). Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be "translated" to a set of "if-then rules" for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS), was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints). The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules. Results Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 ± 0.02%) and membership functions (3.9%), thus suggesting that the "behavior" of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%. Conclusion The study demonstrated a feasible way to automatically perform parameter optimization of inverse treatment planning under guidance of prior knowledge without human intervention other than providing a set of constraints that have proven clinically useful in a given setting.
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Affiliation(s)
- Florian Stieler
- Department of Radiation Oncology, University Medical Center Mannheim, University of Heidelberg, 68167 Mannheim, Germany.
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Pardo-Montero J, Fenwick JD. An approach to multiobjective optimization of rotational therapy. Med Phys 2009; 36:3292-303. [PMID: 19673225 DOI: 10.1118/1.3151806] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Multiobjective optimization is used in radiotherapy, especially IMRT, to generate treatment plans which meet different objectives to varying extents. Trade-off surfaces can be constructed representing the gains and losses of different objectives when switching from one plan to another, and the planner can interactively explore different treatment possibilities without the need for reoptimization. In this work a method for the multiobjective optimization of rotational therapy is introduced. The proposed method is applied slice per slice and uses the geometry of the slice directly to construct several arcs, each conformally irradiating the tumor and blocking a number (0,1,2,...) of different organs at risk present in the treatment. The blocked arc dose distributions so obtained are quite inhomogeneous in the target. An algorithm, based on the iterative reconstruction of images from projections, has been developed to compensate for this inhomogeneity, leading to compensated blocked arcs which deliver more uniform target doses but still block critical structures. Different treatments can be obtained as linear combinations of these arcs, each involving different trade-offs among the objectives involved. The compensatory algorithm substantially improves the target dose uniformity of blocked arcs at the cost of slightly increasing the dose to the rest of the body, allowing delivery of good uniform dose distributions to the target without significantly irradiating the blocked organ(s). Trade-off surfaces are presented for slices containing a target and one or two critical structures. The method is directly implementable using axial or helical tomotherapy. Implementation for conventional linear accelerators will be more difficult because the number of arcs needed to deliver such treatments can be large, an issue to be explored in future work.
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Affiliation(s)
- Juan Pardo-Montero
- School of Cancer Studies, University of Liverpool, Liverpool L69 7ZE, United Kingdom.
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Multicriteria optimization in intensity-modulated radiation therapy treatment planning for locally advanced cancer of the pancreatic head. Int J Radiat Oncol Biol Phys 2008; 72:1208-14. [PMID: 18954714 DOI: 10.1016/j.ijrobp.2008.07.015] [Citation(s) in RCA: 59] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2008] [Revised: 07/11/2008] [Accepted: 07/20/2008] [Indexed: 01/27/2023]
Abstract
PURPOSE Intensity-modulated radiation therapy (IMRT) affords the potential to decrease radiation therapy-associated toxicity by creating highly conformal dose distributions. However, the inverse planning process can create a suboptimal plan despite meeting all constraints. Multicriteria optimization (MCO) may reduce the time-consuming iteration loop necessary to develop a satisfactory plan while providing information regarding trade-offs between different treatment planning goals. In this exploratory study, we examine the feasibility and utility of MCO in physician plan selection in patients with locally advanced pancreatic cancer (LAPC). METHODS AND MATERIALS The first 10 consecutive patients with LAPC treated with IMRT were evaluated. A database of plans (Pareto surface) was created that met the inverse planning goals. The physician then navigated to an "optimal" plan from the point on the Pareto surface at which kidney dose was minimized. RESULTS Pareto surfaces were created for all 10 patients. A physician was able to select a plan from the Pareto surface within 10 minutes for all cases. Compared with the original (treated) IMRT plans, the plan selected from the Pareto surface had a lower stomach mean dose in 9 of 10 patients, although often at the expense of higher kidney dose than with the treated plan. CONCLUSION The MCO is feasible in patients with LAPC and allows the physician to choose a satisfactory plan quickly. Generally, when given the opportunity, the physician will choose a plan with a lower stomach dose. The MCO enables a physician to provide greater active clinical input into the IMRT planning process.
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Lu R, Radke RJ, Yang J, Happersett L, Yorke E, Jackson A. Reduced-order constrained optimization in IMRT planning. Phys Med Biol 2008; 53:6749-66. [PMID: 18997270 DOI: 10.1088/0031-9155/53/23/007] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This paper presents a new algorithm for constrained intensity-modulated radiotherapy (IMRT) planning, made tractable by a dimensionality reduction using a set of plans obtained by fast, unconstrained optimizations. The main result is to reduce planning time by an order of magnitude, producing viable five field prostate IMRT plans in about 5 min. Broadly, the algorithm has three steps. First, we solve a series of independent unconstrained minimization problems based on standard penalty-based objective functions, 'probing' the space of reasonable beamlet intensities. Next, we apply principal component analysis (PCA) to this set of plans, revealing that the high-dimensional intensity space can be spanned by only a few basis vectors. Finally, we parameterize an IMRT plan as a linear combination of these few basis vectors, enabling the fast solution of a constrained optimization problem for the desired intensities. We describe a simple iterative process for handling the dose-volume constraints that are typically required for clinical evaluation, and demonstrate that the resulting plans meet all clinical constraints based on an approximate dose calculation algorithm.
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Affiliation(s)
- Renzhi Lu
- Electrical, Computer, and Systems Engineering Department, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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Sobotta B, Söhn M, Pütz M, Alber M. Tools for the analysis of dose optimization: III. Pointwise sensitivity and perturbation analysis. Phys Med Biol 2008; 53:6337-43. [DOI: 10.1088/0031-9155/53/22/005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Orton CG, Bortfeld TR, Niemierko A, Unkelbach J. The role of medical physicists and the AAPM in the development of treatment planning and optimization. Med Phys 2008; 35:4911-23. [DOI: 10.1118/1.2990777] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Abstract
Sophisticated radiotherapy techniques like intensity modulated radiotherapy with photons and protons rely on numerical dose optimisation. The evaluation of normal tissue dose distributions that deviate significantly from the common clinical routine and also the mathematical expression of desirable properties of a dose distribution is difficult. In essence, a dose evaluation model for normal tissues has to express the tissue specific volume effect. A formalism of local dose effect measures is presented, which can be applied to serial and parallel responding tissues as well as target volumes and physical dose penalties. These models allow a transparent description of the volume effect and an efficient control over the optimum dose distribution. They can be linked to normal tissue complication probability models and the equivalent uniform dose concept. In clinical applications, they provide a means to standardize normal tissue doses in the face of inevitable anatomical differences between patients and a vastly increased freedom to shape the dose, without being overly limiting like sets of dose-volume constraints.
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Affiliation(s)
- Markus Alber
- Sektion für Biomedizinische Physik, Uniklinik für Radioonkologie Tübingen, Tübingen.
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Rosenthal DI, Chambers MS, Fuller CD, Rebueno NCS, Garcia J, Kies MS, Morrison WH, Ang KK, Garden AS. Beam path toxicities to non-target structures during intensity-modulated radiation therapy for head and neck cancer. Int J Radiat Oncol Biol Phys 2008; 72:747-55. [PMID: 18455324 DOI: 10.1016/j.ijrobp.2008.01.012] [Citation(s) in RCA: 144] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2007] [Revised: 12/21/2007] [Accepted: 01/07/2008] [Indexed: 11/18/2022]
Abstract
BACKGROUND Intensity-modulated radiation therapy (IMRT) beams traverse nontarget normal structures not irradiated during three-dimensional conformal RT (3D-CRT) for head and neck cancer (HNC). This study estimates the doses and toxicities to nontarget structures during IMRT. MATERIALS AND METHODS Oropharyngeal cancer IMRT and 3D-CRT cases were reviewed. Dose-volume histograms (DVH) were used to evaluate radiation dose to the lip, cochlea, brainstem, occipital scalp, and segments of the mandible. Toxicity rates were compared for 3D-CRT, IMRT alone, or IMRT with concurrent cisplatin. Descriptive statistics and exploratory recursive partitioning analysis were used to estimate dose "breakpoints" associated with observed toxicities. RESULTS A total of 160 patients were evaluated for toxicity; 60 had detailed DVH evaluation and 15 had 3D-CRT plan comparison. Comparing IMRT with 3D-CRT, there was significant (p </= 0.002) nonparametric differential dose to all clinically significant structures of interest. Thirty percent of IMRT patients had headaches and 40% had occipital scalp alopecia. A total of 76% and 38% of patients treated with IMRT alone had nausea and vomiting, compared with 99% and 68%, respectively, of those with concurrent cisplatin. IMRT had a markedly distinct toxicity profile than 3D-CRT. In recursive partitioning analysis, National Cancer Institute's Common Toxicity Criteria adverse effects 3.0 nausea and vomiting, scalp alopecia and anterior mucositis were associated with reconstructed mean brainstem dose >36 Gy, occipital scalp dose >30 Gy, and anterior mandible dose >34 Gy, respectively. CONCLUSIONS Dose reduction to specified structures during IMRT implies an increased beam path dose to alternate nontarget structures that may result in clinical toxicities that were uncommon with previous, less conformal approaches. These findings have implications for IMRT treatment planning and research, toxicity assessment, and multidisciplinary patient management.
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Affiliation(s)
- David I Rosenthal
- Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
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Schlaefer A, Schweikard A. Stepwise multi-criteria optimization for robotic radiosurgery. Med Phys 2008; 35:2094-103. [DOI: 10.1118/1.2900716] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Craft D, Halabi T, Shih HA, Bortfeld T. An approach for practical multiobjective IMRT treatment planning. Int J Radiat Oncol Biol Phys 2007; 69:1600-7. [PMID: 17920782 DOI: 10.1016/j.ijrobp.2007.08.019] [Citation(s) in RCA: 77] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2007] [Revised: 07/31/2007] [Accepted: 08/01/2007] [Indexed: 10/22/2022]
Abstract
PURPOSE To introduce and demonstrate a practical multiobjective treatment planning procedure for intensity-modulated radiation therapy (IMRT) planning. METHODS AND MATERIALS The creation of a database of Pareto optimal treatment plans proceeds in two steps. The first step solves an optimization problem that finds a single treatment plan which is close to a set of clinical aspirations. This plan provides an example of what is feasible, and is then used to determine mutually satisfiable hard constraints for the subsequent generation of the plan database. All optimizations are done using linear programming. RESULTS The two-step procedure is applied to a brain, a prostate, and a lung case. The plan databases created allow for the selection of a final treatment plan based on the observed tradeoffs between the various organs involved. CONCLUSIONS The proposed method reduces the human iteration time common in IMRT treatment planning. Additionally, the database of plans, when properly viewed, allows the decision maker to make an informed final plan selection.
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Affiliation(s)
- David Craft
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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