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Qi M, Li Y, Wu A, Jia Q, Guo F, Lu X, Kong F, Mai Y, Zhou L, Song T. Region-specific three-dimensional dose distribution prediction: a feasibility study on prostate VMAT cases. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2020. [DOI: 10.1080/16878507.2020.1756185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- M. Qi
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Y. Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - A. Wu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Q. Jia
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - F. Guo
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - X. Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - F. Kong
- Department of Radiation Oncology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Y. Mai
- Department of Oncology, Center People’s Hospital of Zhanjiang, Zhanjiang, China
| | - L. Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - T. Song
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
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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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3
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Creemers IHP, Kusters JMAM, van Kollenburg PGM, Bouwmans LCW, Schinagl DAX, Bussink J. Comparison of dose metrics between automated and manual radiotherapy planning for advanced stage non-small cell lung cancer with volumetric modulated arc therapy. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2019; 9:92-96. [PMID: 33458432 PMCID: PMC7807870 DOI: 10.1016/j.phro.2019.03.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 03/04/2019] [Accepted: 03/06/2019] [Indexed: 12/25/2022]
Affiliation(s)
- Iris H P Creemers
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Johannes M A M Kusters
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | - Liza C W Bouwmans
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Dominic A X Schinagl
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands
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SBRT planning for spinal metastasis: indications from a large multicentric study. Strahlenther Onkol 2018; 195:226-235. [PMID: 30353349 DOI: 10.1007/s00066-018-1383-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 10/08/2018] [Indexed: 12/31/2022]
Abstract
BACKGROUND The dosimetric variability in spine stereotactic body radiation therapy (SBRT) planning was investigated in a large number of centres to identify crowd knowledge-based solutions. METHODS Two spinal cases were planned by 48 planners (38 centres). The required prescription dose (PD) was 3 × 10 Gy and the planning target volume (PTV) coverage request was: VPD > 90% (minimum request: VPD > 80%). The dose constraints were: planning risk volume (PRV) spinal cord: V18Gy < 0.35 cm3, V21.9 Gy < 0.03 cm3; oesophagus: V17.7 Gy < 5 cm3, V25.2 Gy < 0.03 cm3. Planners who did not fulfil the protocol requirements were asked to re-optimize the plans, using the results of planners with the same technology. Statistical analysis was performed to assess correlations between dosimetric results and planning parameters. A quality index (QI) was defined for scoring plans. RESULTS In all, 12.5% of plans did not meet the protocol requirements. After re-optimization, 98% of plans fulfilled the constraints, showing the positive impact of knowledge sharing. Statistical analysis showed a significant correlation (p < 0.05) between the homogeneity index (HI) and PTV coverage for both cases, while the correlation between HI and spinal cord sparing was significant only for the single dorsal PTV case. Moreover, the multileaf collimator leaf thickness correlated with the spinal cord sparing. Planners using comparable delivery/planning system techniques produced different QI, highlighting the impact of the planner's skills in the optimization process. CONCLUSION Both the technology and the planner's skills are fundamentally important in spine SBRT planning optimization. Knowledge sharing helped to follow the plan objectives.
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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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Shah VP, Lakshminarayanan P, Moore J, Tran PT, Quon H, Deville C, McNutt TR. Data integrity systems for organ contours in radiation therapy planning. J Appl Clin Med Phys 2018; 19:58-67. [PMID: 29893465 PMCID: PMC6036377 DOI: 10.1002/acm2.12353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Revised: 01/31/2018] [Accepted: 03/27/2018] [Indexed: 11/30/2022] Open
Abstract
The purpose of this research is to develop effective data integrity models for contoured anatomy in a radiotherapy workflow for both real-time and retrospective analysis. Within this study, two classes of contour integrity models were developed: data driven models and contiguousness models. The data driven models aim to highlight contours which deviate from a gross set of contours from similar disease sites and encompass the following regions of interest (ROI): bladder, femoral heads, spinal cord, and rectum. The contiguousness models, which individually analyze the geometry of contours to detect possible errors, are applied across many different ROI's and are divided into two metrics: Extent and Region Growing over volume. After analysis, we found that 70% of detected bladder contours were verified as suspicious. The spinal cord and rectum models verified that 73% and 80% of contours were suspicious respectively. The contiguousness models were the most accurate models and the Region Growing model was the most accurate submodel. 100% of the detected noncontiguous contours were verified as suspicious, but in the cases of spinal cord, femoral heads, bladder, and rectum, the Region Growing model detected additional two to five suspicious contours that the Extent model failed to detect. When conducting a blind review to detect false negatives, it was found that all the data driven models failed to detect all suspicious contours. The Region Growing contiguousness model produced zero false negatives in all regions of interest other than prostate. With regards to runtime, the contiguousness via extent model took an average of 0.2 s per contour. On the other hand, the region growing method had a longer runtime which was dependent on the number of voxels in the contour. Both contiguousness models have potential for real-time use in clinical radiotherapy while the data driven models are better suited for retrospective use.
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Affiliation(s)
- Veeraj P Shah
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Joseph Moore
- Department of Medical Oncology and Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Phuoc T Tran
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Medical Oncology and Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Harry Quon
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Curtiland Deville
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Todd R McNutt
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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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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Künzel LA, Dohm OS, Alber M, Zips D, Thorwarth D. Automatic replanning of VMAT plans for different treatment machines: A template-based approach using constrained optimization. Strahlenther Onkol 2018; 194:921-928. [PMID: 29846751 DOI: 10.1007/s00066-018-1319-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 05/12/2018] [Indexed: 11/26/2022]
Abstract
PURPOSE To investigate a new automatic template-based replanning approach combined with constrained optimization, which may be highly useful for a rapid plan transfer for planned or unplanned machine breakdowns. This approach was tested for prostate cancer (PC) and head-and-neck cancer (HNC) cases. METHODS The constraints of a previously optimized volumetric modulated arc therapy (VMAT) plan were used as a template for automatic plan reoptimization for different accelerator head models. All plans were generated using the treatment planning system (TPS) Hyperion. Automatic replanning was performed for 16 PC cases, initially planned for MLC1 (4 mm MLC) and reoptimized for MLC2 (5 mm) and MLC3 (10 mm) and for 19 HNC cases, replanned from MLC2 to MLC3. EUD, Dmean, D2%, and D98% were evaluated for targets; for OARs EUD and D2% were analyzed. Replanning was considered successful if both plans fulfilled equal constraints. RESULTS All prostate cases were successfully replanned. The mean relative target EUD deviation was -0.15% and -0.57% for replanning to MLC2 and MLC3, respectively. OAR sparing was successful in all cases. Replanning of HNC cases from MLC2 to MLC3 was successful in 16/19 patients with a mean decrease of -0.64% in PTV60 EUD. In three cases target doses were substantially decreased by up to -2.58% (PTV60) and -3.44% (PTV54), respectively. Nevertheless, OAR sparing was always achieved as planned. CONCLUSIONS Automatic replanning of VMAT plans for a different treatment machine by using pre-existing constraints as a template for a reoptimization is feasible and successful in terms of equal constraints.
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Affiliation(s)
- Luise A Künzel
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Oliver S Dohm
- Department of Radiation Oncology, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Markus Alber
- Radiation Oncology, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Daniel Zips
- Department of Radiation Oncology, University Hospital Tübingen, 72076, Tübingen, Germany
- German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, 72076, Tübingen, Germany.
- German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Powis R, Bird A, Brennan M, Hinks S, Newman H, Reed K, Sage J, Webster G. Clinical implementation of a knowledge based planning tool for prostate VMAT. Radiat Oncol 2017; 12:81. [PMID: 28482845 PMCID: PMC5423022 DOI: 10.1186/s13014-017-0814-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2017] [Accepted: 04/25/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A knowledge based planning tool has been developed and implemented for prostate VMAT radiotherapy plans providing a target average rectum dose value based on previously achievable values for similar rectum/PTV overlap. The purpose of this planning tool is to highlight sub-optimal clinical plans and to improve plan quality and consistency. METHODS A historical cohort of 97 VMAT prostate plans was interrogated using a RayStation script and used to develop a local model for predicting optimum average rectum dose based on individual anatomy. A preliminary validation study was performed whereby historical plans identified as "optimal" and "sub-optimal" by the local model were replanned in a blinded study by four experienced planners and compared to the original clinical plan to assess whether any improvement in rectum dose was observed. The predictive model was then incorporated into a RayStation script and used as part of the clinical planning process. Planners were asked to use the script during planning to provide a patient specific prediction for optimum average rectum dose and to optimise the plan accordingly. RESULTS Plans identified as "sub-optimal" in the validation study observed a statistically significant improvement in average rectum dose compared to the clinical plan when replanned whereas plans that were identified as "optimal" observed no improvement when replanned. This provided confidence that the local model can identify plans that were suboptimal in terms of rectal sparing. Clinical implementation of the knowledge based planning tool reduced the population-averaged mean rectum dose by 5.6Gy. There was a small but statistically significant increase in total MU and femoral head dose and a reduction in conformity index. These did not affect the clinical acceptability of the plans and no significant changes to other plan quality metrics were observed. CONCLUSIONS The knowledge-based planning tool has enabled substantial reductions in population-averaged mean rectum dose for prostate VMAT patients. This suggests plans are improved when planners receive quantitative feedback on plan quality against historical data.
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Affiliation(s)
- Richard Powis
- Worcestershire Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - Andrew Bird
- Worcestershire Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - Matthew Brennan
- Worcestershire Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - Susan Hinks
- Worcestershire Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - Hannah Newman
- Worcestershire Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - Katie Reed
- Worcestershire Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - John Sage
- Worcestershire Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
- Centre for Technology Enabled Health Care, Coventry University, Coventry, UK
| | - Gareth Webster
- Worcestershire Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
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