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Title: Vector-model-supported approach in prostate plan optimization

Abstract

Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planningmore » time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration number without compromising the plan quality.« less

Authors:
 [1];  [2];  [3];  [1]; ;  [1];  [4];  [3]
  1. Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia)
  2. (Hong Kong)
  3. Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong)
  4. (Australia)
Publication Date:
OSTI Identifier:
22685186
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Dosimetry; Journal Volume: 42; Journal Issue: 2; Other Information: Copyright (c) 2017 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
61 RADIATION PROTECTION AND DOSIMETRY; 62 RADIOLOGY AND NUCLEAR MEDICINE; BYPASSES; COMMUNICATIONS; COMPARATIVE EVALUATIONS; DRUGS; ERRORS; HEAD; MANUALS; MEDICAL PERSONNEL; NEOPLASMS; OPTIMIZATION; PLANNING; PROSTATE; RADIATION DOSES; RADIOTHERAPY; VECTORS

Citation Formats

Liu, Eva Sau Fan, Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Wu, Vincent Wing Cheung, Harris, Benjamin, Lehman, Margot, Pryor, David, School of Medicine, University of Queensland, and Chan, Lawrence Wing Chi, E-mail: wing.chi.chan@polyu.edu.hk. Vector-model-supported approach in prostate plan optimization. United States: N. p., 2017. Web. doi:10.1016/J.MEDDOS.2017.01.001.
Liu, Eva Sau Fan, Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Wu, Vincent Wing Cheung, Harris, Benjamin, Lehman, Margot, Pryor, David, School of Medicine, University of Queensland, & Chan, Lawrence Wing Chi, E-mail: wing.chi.chan@polyu.edu.hk. Vector-model-supported approach in prostate plan optimization. United States. doi:10.1016/J.MEDDOS.2017.01.001.
Liu, Eva Sau Fan, Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Wu, Vincent Wing Cheung, Harris, Benjamin, Lehman, Margot, Pryor, David, School of Medicine, University of Queensland, and Chan, Lawrence Wing Chi, E-mail: wing.chi.chan@polyu.edu.hk. Sat . "Vector-model-supported approach in prostate plan optimization". United States. doi:10.1016/J.MEDDOS.2017.01.001.
@article{osti_22685186,
title = {Vector-model-supported approach in prostate plan optimization},
author = {Liu, Eva Sau Fan and Department of Health Technology and Informatics, The Hong Kong Polytechnic University and Wu, Vincent Wing Cheung and Harris, Benjamin and Lehman, Margot and Pryor, David and School of Medicine, University of Queensland and Chan, Lawrence Wing Chi, E-mail: wing.chi.chan@polyu.edu.hk},
abstractNote = {Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration number without compromising the plan quality.},
doi = {10.1016/J.MEDDOS.2017.01.001},
journal = {Medical Dosimetry},
number = 2,
volume = 42,
place = {United States},
year = {Sat Jul 01 00:00:00 EDT 2017},
month = {Sat Jul 01 00:00:00 EDT 2017}
}
  • Long planning time in volumetric-modulated arc stereotactic radiotherapy (VMA-SRT) cases can limit its clinical efficiency and use. A vector model could retrieve previously successful radiotherapy cases that share various common anatomic features with the current case. The prsent study aimed to develop a vector model that could reduce planning time by applying the optimization parameters from those retrieved reference cases. Thirty-six VMA-SRT cases of brain metastasis (gender, male [n = 23], female [n = 13]; age range, 32 to 81 years old) were collected and used as a reference database. Another 10 VMA-SRT cases were planned with both conventional optimization and vector-model-supported optimization, followingmore » the oncologists' clinical dose prescriptions. Planning time and plan quality measures were compared using the 2-sided paired Wilcoxon signed rank test with a significance level of 0.05, with positive false discovery rate (pFDR) of less than 0.05. With vector-model-supported optimization, there was a significant reduction in the median planning time, a 40% reduction from 3.7 to 2.2 hours (p = 0.002, pFDR = 0.032), and for the number of iterations, a 30% reduction from 8.5 to 6.0 (p = 0.006, pFDR = 0.047). The quality of plans from both approaches was comparable. From these preliminary results, vector-model-supported optimization can expedite the optimization of VMA-SRT for brain metastasis while maintaining plan quality.« less
  • Purpose: To describe rectal dose reduction achieved and techniques used to take advantage of the increased peri-rectal spacing provided by injected polyethylene-glycol. Methods: Thirty prostate cancer patents were 2:1 randomized during a clinical trial to evaluate the effectiveness of injected poly-ethylene glycol hydrogel (SpaceOAR System) in creating space between the prostate and the anterior rectal wall. All patients received a baseline CT/MR scan and baseline IMRT treatment plan. Patients were randomized to receive hydrogel injection (n=20) or Control (n=10), followed by another CT/MR scan and treatment plan (single arc VMAT, 6 MV photons, 79.2 Gy, 44 fractions). Additional optimization structuresmore » were employed to constrain the dose to the rectum; specifically an avoidance structure to limit V75 <15%, and a control structure to limit the maximum relative dose <105% in the interface region of the anterior rectal wall and the prostate planning target volume. Dose volumetric data was analyzed for rectal volumes receiving 60 through 80 Gy. Results: Rectal dose reduction was observed in all patients who received the hydrogel. Volumetric analysis indicates a median rectal volume and (reduction from baseline plan) following spacer application of 4.9% (8.9%) at V60Gy, 3.8% (8.1%) at V65Gy, 2.5% (7.2%) at V70Gy, 1.6% (5.8%) at V75Gy, and 0.5% (2.5%) at V80Gy. Conclusion: Relative to planning without spacers, rectal dose constraints of 5%, 4%, 3%, 2%, 1% for V60, V65, V70, V75, and V80, should be obtainable when peri-rectal spacers are used. The combined effect of increased peri-rectal space provided by the hydrogel, with strict optimization objectives, resulted in reduced dose to the rectum. To maximize benefit, strict optimization objectives and reduced rectal dose constraints should be employed when creating plans for patients with perirectal spacers. Clinical Trial for SpaceOAR product conducted by Augmenix,Inc. The research site was paid to be a participating site.« less
  • The purpose of this study is to evaluate the influence of treatment-planning parameters on the quality of treatment plans in tomotherapy and to find the optimized planning parameter combinations when treating patients with prostate cancer under different performances. A total of 3 patients with prostate cancer with Eastern Cooperative Oncology Group (ECOG) performance status of 2 or 3 were included in this study. For each patient, 27 treatment plans were created using a combination of planning parameters (field width of 1, 2.5, and 5 cm; pitch of 0.172, 0.287, and 0.43; and modulation factor of 1.8, 3, and 3.5). Then,more » plans were analyzed using several dosimetrical indices: the prescription isodose to target volume (PITV) ratio, homogeneity index (HI), conformity index (CI), target coverage index (TCI), modified dose HI (MHI), conformity number (CN), and quality factor (QF). Furthermore, dose-volume histogram of critical structures and critical organ scoring index (COSI) were used to analyze organs at risk (OAR) sparing. Interestingly, treatment plans with a field width of 1 cm showed more favorable results than others in the planning target volume (PTV) and OAR indices. However, the treatment time of the 1-cm field width was 3 times longer than that of plans with a field width of 5 cm. There was no substantial decrease in treatment time when the pitch was increased from 0.172 to 0.43, but the PTV indices were slightly compromised. As expected, field width had the most significant influence on all of the indices including PTV, OAR, and treatment time. For the patients with good performance who can tolerate a longer treatment time, we suggest a field width of 1 cm, pitch of 0.172, and modulation factor of 1.8; for the patients with poor performance status, field width of 5 cm, pitch of 0.287, and a modulation factor of 3.5 should be considered.« less
  • Purpose: To optimize dose distribution for high-dose-rate brachytherapy for prostate cancer, we have developed a new algorithm named Attraction-Repulsion Model (ARM). In this study, we compared the ARM with geometric optimization (GO). Methods and Materials: The ARM was used to optimize the dose distribution by finding the best dwell time combination. ARM requires grids inside the clinical target volume (CTV) and critical organs. These grids generate attraction or repulsion based on specific dose constraints. After calculations were performed repeatedly until the attraction and repulsion forces reached equilibrium, the optimal dwell time distribution was established. We compared the ARM with GOmore » for 10 patients using dose-volume histograms. Results: The CTV ranged from 23 to 48 cc, and the CTV V150 ranged from 52% to 79%, and 23% to 44% for GO and ARM, respectively. This indicates that the dose homogeneity indices, as well as the conformal indices, were higher for ARM than for GO. The urethra V150 was 0-99% and 0-1% for GO and ARM, respectively. Conclusion: The ARM proved to be superior to GO in minimizing the dose to normal structures and in improving dose homogeneity for the target while reducing the dose to normal tissues.« less
  • This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of the paper.« less