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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.
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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.
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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
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Zhu J, Simon A, Haigron P, Lafond C, Acosta O, Shu H, Castelli J, Li B, De Crevoisier R. The benefit of using bladder sub-volume equivalent uniform dose constraints in prostate intensity-modulated radiotherapy planning. Onco Targets Ther 2016; 9:7537-7544. [PMID: 28003767 PMCID: PMC5161391 DOI: 10.2147/ott.s116508] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Background To assess the benefits of bladder wall sub-volume equivalent uniform dose (EUD) constraints in prostate cancer intensity-modulated radiotherapy (IMRT) planning. Methods Two IMRT plans, with and without EUD constraints on the bladder wall, were generated using beams that deliver 80 Gy to the prostate and 46 Gy to the seminal vesicles and were compared in 53 prostate cancer patients. The bladder wall was defined as the volume between the external manually delineated wall and a contraction of 7 mm apart from it. The bladder wall was then separated into two parts: the internal-bladder wall (bla-in) represented by the portion of the bladder wall that intersected with the planning target volume (PTV) plus 5 mm extension; the external-bladder wall (bla-ex) represented by the remaining part of the bladder wall. In the IMRT plan with EUD constraints, the values of “a” parameter for the EUD models were 10.0 for bla-in and 2.3 for bla-ex. The plans with and without EUD constraints were compared in terms of dose–volume histograms, 5-year bladder and rectum normal tissue complication probability values, as well as tumor control probability (TCP) values. Results The use of bladder sub-volume EUD constraints decreased both the doses to the bladder wall (V70: 22.76% vs 19.65%, Dmean: 39.82 Gy vs 35.45 Gy) and the 5-year bladder complication probabilities (≥LENT/SOMA Grade 2: 20.35% vs 17.96%; bladder bleeding: 10.63% vs 8.64%). The doses to the rectum wall and the rectum complication probabilities were also slightly decreased by the EUD constraints compared to physical constraints only. The minimal dose and the V76Gy of PTVprostate were, however, slightly decreased by EUD optimization, nevertheless without significant difference in TCP values between the two plans, and the PTV parameters finally respected the Groupe d’Etude des Tumeurs Uro-Génitales recommendations. Conclusion Separating the bladder wall into two parts with appropriate EUD optimization may reduce bladder toxicity in prostate IMRT. Combining biological constraints with physical constraints in the organs at risk at the inverse planning step of IMRT may improve the dose distribution.
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Affiliation(s)
- Jian Zhu
- Laboratory of Image Science and Technology, Southeast University, Nanjing, Jiangsu; Department of Radiation Oncology, Shandong Cancer Hospital & Institute, Jinan; Centre de Recherche en Information Biomédicale Sino-français, Nanjing, People's Republic of China
| | - Antoine Simon
- Centre de Recherche en Information Biomédicale Sino-français, Nanjing, People's Republic of China; Institut National de la Sante et de la Recherche Medicale, U1099; Laboratory of Signal and Image Processing (LTSI), University of Rennes 1
| | - Pascal Haigron
- Centre de Recherche en Information Biomédicale Sino-français, Nanjing, People's Republic of China; Institut National de la Sante et de la Recherche Medicale, U1099; Laboratory of Signal and Image Processing (LTSI), University of Rennes 1
| | - Caroline Lafond
- Institut National de la Sante et de la Recherche Medicale, U1099; Laboratory of Signal and Image Processing (LTSI), University of Rennes 1; Department of Radiotherapy, Centre Eugène Marquis, Rennes, France
| | - Oscar Acosta
- Institut National de la Sante et de la Recherche Medicale, U1099; Laboratory of Signal and Image Processing (LTSI), University of Rennes 1
| | - Huazhong Shu
- Laboratory of Image Science and Technology, Southeast University, Nanjing, Jiangsu; Centre de Recherche en Information Biomédicale Sino-français, Nanjing, People's Republic of China
| | - Joel Castelli
- Institut National de la Sante et de la Recherche Medicale, U1099; Laboratory of Signal and Image Processing (LTSI), University of Rennes 1; Department of Radiotherapy, Centre Eugène Marquis, Rennes, France
| | - Baosheng Li
- Laboratory of Image Science and Technology, Southeast University, Nanjing, Jiangsu; Department of Radiation Oncology, Shandong Cancer Hospital & Institute, Jinan; Centre de Recherche en Information Biomédicale Sino-français, Nanjing, People's Republic of China
| | - Renaud De Crevoisier
- Centre de Recherche en Information Biomédicale Sino-français, Nanjing, People's Republic of China; Institut National de la Sante et de la Recherche Medicale, U1099; Laboratory of Signal and Image Processing (LTSI), University of Rennes 1; Department of Radiotherapy, Centre Eugène Marquis, Rennes, France
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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
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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.
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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
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