1
|
Anetai Y, Takegawa H, Koike Y, Nakamura S, Tanigawa N. Effective optimization strategy for large optimization volume object, remaining volume at risk (RVR): α-value selection and usage from generalized equivalent uniform dose (gEUD) curve deviation perspective. Phys Med Biol 2023; 68. [PMID: 36745933 DOI: 10.1088/1361-6560/acb989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 02/06/2023] [Indexed: 02/08/2023]
Abstract
Objective.A large optimization volume for intensity-modulated radiation therapy (IMRT), such as the remaining volume at risk (RVR), is traditionally unsuitable for dose-volume constraint control and requires planner-specific empirical considerations owing to the patient-specific shape. To enable less empirical optimization, the generalized equivalent uniform dose (gEUD) optimization is effective; however, the utilization of parametera-values remains elusive. Our study clarifies thea-value characteristics for optimization and to enable effectivea-value use.Approach.The gEUD can be obtained as a function of itsa-value, which is the weighted generalized mean; its curve has a continuous, differentiable, and sigmoid shape, deforming in its optimization state with retained curve characteristics. Using differential geometry, the gEUD curve changes in optimization is considered a geodesic deviation intervened by the forces between deforming and retaining the curve. The curvature and gradient of the curve are radically related to optimization. The vertex point (a=ak) was set and thea-value roles were classified into the following three parts of the curve with respect to thea-value: (i) high gradient and middle curvature, (ii) middle gradient and high curvature, and (iii) low gradient and low curvature. Then, a strategy for multiplea-values was then identified using RVR optimization.Main results.Eleven head and neck patients who underwent static seven-field IMRT were used to verify thea-value characteristics and curvature effect for optimization. The lowera-value (i) (a= 1-3) optimization was effective for the whole dose-volume range; in contrast, the effect of highera-value (iii) (a= 12-20) optimization addressed strongly the high-dose range of the dose volume. The middlea-value (ii) (arounda=ak) showed intermediate but effective high-to-low dose reduction. Thesea-value characteristics were observed as superimpositions in the optimization. Thus, multiple gEUD-based optimization was significantly superior to the exponential constraints normally applied to the RVR that surrounds the PTV, normal tissue objective (NTO), resulting in up to 25.9% and 8.1% improvement in dose-volume indices D2% and V10Gy, respectively.Significance.This study revealed an appropriatea-value for gEUD optimization, leading to favorable dose-volume optimization for the RVR region using fixed multiplea-value conditions, despite the very large and patient-specific shape of the region.
Collapse
Affiliation(s)
- Yusuke Anetai
- Department of Radiology, Kansai Medical University, Shin-machi 2-5-1, Hirakata-shi, Osaka 573-1010, Japan
| | - Hideki Takegawa
- Department of Radiology, Kansai Medical University, Shin-machi 2-5-1, Hirakata-shi, Osaka 573-1010, Japan
| | - Yuhei Koike
- Department of Radiology, Kansai Medical University, Shin-machi 2-5-1, Hirakata-shi, Osaka 573-1010, Japan
| | - Satoaki Nakamura
- Department of Radiology, Kansai Medical University, Shin-machi 2-5-1, Hirakata-shi, Osaka 573-1010, Japan
| | - Noboru Tanigawa
- Department of Radiology, Kansai Medical University, Shin-machi 2-5-1, Hirakata-shi, Osaka 573-1010, Japan
| |
Collapse
|
2
|
Fallahi A, Mahnam M, Niaki STA. A discrete differential evolution with local search particle swarm optimization to direct angle and aperture optimization in IMRT treatment planning problem. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
|
3
|
Split Common Coincidence Point Problem: A Formulation Applicable to (Bio)Physically-Based Inverse Planning Optimization. Symmetry (Basel) 2020. [DOI: 10.3390/sym12122086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Inverse planning is a method of radiotherapy treatment planning where the care team begins with the desired dose distribution satisfying prescribed clinical objectives, and then determines the treatment parameters that will achieve it. The variety in symmetry, form, and characteristics of the objective functions describing clinical criteria requires a flexible optimization approach in order to obtain optimized treatment plans. Therefore, we introduce and discuss a nonlinear optimization formulation called the split common coincidence point problem (SCCPP). We show that the SCCPP is a suitable formulation for the inverse planning optimization problem with the flexibility of accommodating several biological and/or physical clinical objectives. Also, we propose an iterative algorithm for approximating the solution of the SCCPP, and using Bregman techniques, we establish that the proposed algorithm converges to a solution of the SCCPP and to an extremum of the inverse planning optimization problem. We end with a note on useful insights on implementing the algorithm in a clinical setting.
Collapse
|
4
|
Guo C, Zhang P, Gui Z, Shu H, Zhai L, Xu J. Prescription Value-Based Automatic Optimization of Importance Factors in Inverse Planning. Technol Cancer Res Treat 2019; 18:1533033819892259. [PMID: 31782353 PMCID: PMC6886287 DOI: 10.1177/1533033819892259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Objective: An automatic method for the optimization of importance factors was proposed to improve the efficiency of inverse planning. Methods: The automatic method consists of 3 steps: (1) First, the importance factors are automatically and iteratively adjusted based on our proposed penalty strategies. (2) Then, plan evaluation is performed to determine whether the obtained plan is acceptable. (3) If not, a higher penalty is assigned to the unsatisfied objective by multiplying it by a compensation coefficient. The optimization processes are performed alternately until an acceptable plan is obtained or the maximum iteration Nmax of step (3) is reached. Results: Tested on 2 kinds of clinical cases and compared with manual method, the results showed that the quality of the proposed automatic plan was comparable to, or even better than, the manual plan in terms of the dose–volume histogram and dose distributions. Conclusions: The proposed algorithm has potential to significantly improve the efficiency of the existing manual adjustment methods for importance factors and contributes to the development of fully automated planning. Especially, the more the subobjective functions, the more obvious the advantage of our algorithm.
Collapse
Affiliation(s)
- Caiping Guo
- Department of Electronic Engineering, Taiyuan Institute of Technology, Taiyuan, China.,Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data, North University of China, Taiyuan, China
| | - Pengcheng Zhang
- Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data, North University of China, Taiyuan, China
| | - Zhiguo Gui
- Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data, North University of China, Taiyuan, China
| | - Huazhong Shu
- Laboratory of Image Science and Technology, Southeast University, Nanjing, China.,Centre de Recherche en Information Médicale Sino-français (CRIBs), Rennes, France
| | - Lihong Zhai
- Department of Electronic Engineering, Taiyuan Institute of Technology, Taiyuan, China
| | - Jinrong Xu
- Department of Electronic Engineering, Taiyuan Institute of Technology, Taiyuan, China
| |
Collapse
|
5
|
Brüningk SC, Kamp F, Wilkens JJ. EUD‐based biological optimization for carbon ion therapy. Med Phys 2015; 42:6248-57. [DOI: 10.1118/1.4932219] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Sarah C. Brüningk
- Department of Radiation Oncology, Technische Universität München, Klinikum rechts der Isar, Ismaninger Str. 22, München 81675, Germany and Physik‐Department, Technische Universität München, James‐Franck‐Str. 1, Garching 85748, Germany
| | - Florian Kamp
- Department of Radiation Oncology, Technische Universität München, Klinikum rechts der Isar, Ismaninger Str. 22, München 81675, Germany and Physik‐Department, Technische Universität München, James‐Franck‐Str. 1, Garching 85748, Germany
| | - Jan J. Wilkens
- Department of Radiation Oncology, Technische Universität München, Klinikum rechts der Isar, Ismaninger Str. 22, München 81675, Germany and Physik‐Department, Technische Universität München, James‐Franck‐Str. 1, Garching 85748, Germany
| |
Collapse
|
6
|
Kierkels RGJ, Korevaar EW, Steenbakkers RJHM, Janssen T, van't Veld AA, Langendijk JA, Schilstra C, van der Schaaf A. Direct use of multivariable normal tissue complication probability models in treatment plan optimisation for individualised head and neck cancer radiotherapy produces clinically acceptable treatment plans. Radiother Oncol 2014; 112:430-6. [PMID: 25220369 DOI: 10.1016/j.radonc.2014.08.020] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Revised: 08/11/2014] [Accepted: 08/13/2014] [Indexed: 10/24/2022]
Abstract
BACKGROUND AND PURPOSE Recently, clinically validated multivariable normal tissue complication probability models (NTCP) for head and neck cancer (HNC) patients have become available. We test the feasibility of using multivariable NTCP-models directly in the optimiser for inverse treatment planning of radiotherapy to improve the dose distributions and corresponding NTCP-estimates in HNC patients. MATERIAL AND METHODS For 10 HNC cases, intensity-modulated radiotherapy plans were optimised either using objective functions based on the 'generalised equivalent uniform dose' (OFgEUD) or based on multivariable NTCP-models (OFNTCP). NTCP-models for patient-rated xerostomia, physician-rated RTOG grade II-IV dysphagia, and various patient-rated aspects of swallowing dysfunction were incorporated. The NTCP-models included dose-volume parameters as well as clinical factors contributing to a personalised optimisation process. Both optimisation techniques were compared by means of 'pseudo Pareto fronts' (target dose conformity vs. the sum of the NTCPs). RESULTS Both optimisation techniques resulted in clinically realistic treatment plans with only small differences. For nine patients the sum-NTCP was lower for the OFNTCP optimised plans (on average 5.7% (95%CI 1.7-9.9%, p<0.006)). Furthermore, the OFNTCP provided the advantages of fewer unknown optimisation parameters and an intrinsic mechanism of individualisation. CONCLUSIONS Treatment plan optimisation using multivariable NTCP-models directly in the OF is feasible as has been demonstrated for HNC radiotherapy.
Collapse
Affiliation(s)
- Roel G J Kierkels
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, The Netherlands.
| | - Erik W Korevaar
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, The Netherlands
| | - Roel J H M Steenbakkers
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, The Netherlands
| | - Tomas Janssen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Aart A van't Veld
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, The Netherlands
| | - Johannes A Langendijk
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, The Netherlands
| | - Cornelis Schilstra
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, The Netherlands; Radiotherapeutic Institute Friesland, Leeuwarden, The Netherlands
| | - Arjen van der Schaaf
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, The Netherlands
| |
Collapse
|
7
|
Yao R, Templeton AK, Liao Y, Turian JV, Kiel KD, Chu JC. Optimization for high-dose-rate brachytherapy of cervical cancer with adaptive simulated annealing and gradient descent. Brachytherapy 2014; 13:352-60. [DOI: 10.1016/j.brachy.2013.10.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2013] [Revised: 10/09/2013] [Accepted: 10/29/2013] [Indexed: 01/30/2023]
|
8
|
Advantage of biological over physical optimization in prostate cancer? Z Med Phys 2011; 21:228-35. [DOI: 10.1016/j.zemedi.2011.02.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2010] [Revised: 12/17/2010] [Accepted: 02/02/2011] [Indexed: 11/20/2022]
|
9
|
Morávek Z, Rickhey M, Hartmann M, Bogner L. Uncertainty reduction in intensity modulated proton therapy by inverse Monte Carlo treatment planning. Phys Med Biol 2009; 54:4803-19. [DOI: 10.1088/0031-9155/54/15/011] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
|
10
|
Bogner L, Alt M, Dirscherl T, Morgenstern I, Latscha C, Rickhey M. Fast direct Monte Carlo optimization using the inverse kernel approach. Phys Med Biol 2009; 54:4051-67. [DOI: 10.1088/0031-9155/54/13/007] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
|