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Radici L, Petrucci E, Casanova Borca V, Cante D, Piva C, Pasquino M. Impact of beam complexity on plan delivery accuracy verification of a transmission detector in volumetric modulated arc therapy. Phys Med 2024; 122:103387. [PMID: 38797025 DOI: 10.1016/j.ejmp.2024.103387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 04/22/2024] [Accepted: 05/22/2024] [Indexed: 05/29/2024] Open
Abstract
OBJECTIVE To study the effect of beam complexity on VMAT delivery accuracy evaluated by means of a transmission detector, together with the possibility of scoring plan complexity. METHODS 43 clinical VMAT plans delivered by a TrueBeam linear accelerator to both Delta4 Discover and Delta4 Phantom+ for patient-specific quality assurance were evaluated. Global Dose-γ analysis, MLC-γ analysis, percentage of leaves with a deviation between planned and measured leaf tip position lower than 1 mm (LD) were computed. Modulation complexity score (MCSv), average leaf travel (LT), a multiplicative combination of LT and MCSv (LTMCS), percentage of leaves with speed lower than 5 mm/s (LS), from 5 to 20 mm/s (MS), higher than 20 mm/s (HS) and the average value of leaf speed (MLCSav) were evaluated by means of an home-made Matlab script. RESULTS Dose-γ passing rate showed a moderate correlation with MCSv, LT, MLCSav, LS and HS, while a stronger positive correlation was found with LTMCS. A strong correlation was observed between LD and both LT and leaves speed, while a weak correlation was observed with MCSv. A correlation between MLC-γ pass rate and plan complexity parameters was found except for MCSv; a moderate correlation with LS was observed, while all other parameters showed weak correlations. CONCLUSIONS The study confirmed the possibility to establish correlations between plan complexity indices versus dose distribution and MLC parameters measured by a transmissive detector. Further investigation is necessary to define specific values of the complexity indices to evaluate whether a VMAT plan is deliverable as intended.
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Tan HQ, Lew KS, Wong YM, Chong WC, Koh CWY, Chua CGA, Yeap PL, Ang KW, Lee JCL, Park SY. Detecting outliers beyond tolerance limits derived from statistical process control in patient-specific quality assurance. J Appl Clin Med Phys 2024; 25:e14154. [PMID: 37683120 PMCID: PMC10860546 DOI: 10.1002/acm2.14154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Tolerance limit is defined on pre-treatment patient specific quality assurance results to identify "out of the norm" dose discrepancy in plan. An out-of-tolerance plan during measurement can often cause treatment delays especially if replanning is required. In this study, we aim to develop an outlier detection model to identify out-of-tolerance plan early during treatment planning phase to mitigate the above-mentioned risks. METHODS Patient-specific quality assurance results with portal dosimetry for stereotactic body radiotherapy measured between January 2020 and December 2021 were used in this study. Data were divided into thorax and pelvis sites and gamma passing rates were recorded using 2%/2 mm, 2%/1 mm, and 1%/1 mm gamma criteria. Statistical process control method was used to determine six different site and criterion-specific tolerance and action limits. Using only the inliers identified with our determined tolerance limits, we trained three different outlier detection models using the plan complexity metrics extracted from each treatment field-robust covariance, isolation forest, and one class support vector machine. The hyperparameters were optimized using the F1-score calculated from both the inliers and validation outliers' data. RESULTS 308 pelvis and 200 thorax fields were used in this study. The tolerance (action) limits for 2%/2 mm, 2%/1 mm, and 1%/1 mm gamma criteria in the pelvis site are 99.1% (98.1%), 95.8% (91.1%), and 91.7% (86.1%), respectively. The tolerance (action) limits in the thorax site are 99.0% (98.7%), 97.0% (96.2%), and 91.5% (87.2%). One class support vector machine performs the best among all the algorithms. The best performing model in the thorax (pelvis) site achieves a precision of 0.56 (0.54), recall of 1.0 (1.0), and F1-score of 0.72 (0.70) when using the 2%/2 mm (2%/1 mm) criterion. CONCLUSION The model will help the planner to identify an out-of-tolerance plan early so that they can refine the plan further during the planning stage without risking late discovery during measurement.
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Affiliation(s)
- Hong Qi Tan
- Division of Radiation OncologyNational Cancer Centre SingaporeSingaporeSingapore
- Oncology Academic Clinical ProgrammeDuke‐NUS Medical SchoolSingaporeSingapore
| | - Kah Seng Lew
- Division of Radiation OncologyNational Cancer Centre SingaporeSingaporeSingapore
- Division of Physics and Applied PhysicsNanyang Technological UniversitySingaporeSingapore
| | - Yun Ming Wong
- Division of Physics and Applied PhysicsNanyang Technological UniversitySingaporeSingapore
| | - Wen Chuan Chong
- Division of Radiation OncologyNational Cancer Centre SingaporeSingaporeSingapore
| | - Calvin Wei Yang Koh
- Division of Radiation OncologyNational Cancer Centre SingaporeSingaporeSingapore
| | | | - Ping Lin Yeap
- Division of Radiation OncologyNational Cancer Centre SingaporeSingaporeSingapore
| | - Khong Wei Ang
- Division of Radiation OncologyNational Cancer Centre SingaporeSingaporeSingapore
| | - James Cheow Lei Lee
- Division of Radiation OncologyNational Cancer Centre SingaporeSingaporeSingapore
- Division of Physics and Applied PhysicsNanyang Technological UniversitySingaporeSingapore
| | - Sung Yong Park
- Division of Radiation OncologyNational Cancer Centre SingaporeSingaporeSingapore
- Oncology Academic Clinical ProgrammeDuke‐NUS Medical SchoolSingaporeSingapore
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Noblet C, Maunet M, Duthy M, Coste F, Moreau M. A TPS integrated machine learning tool for predicting patient-specific quality assurance outcomes in volumetric-modulated arc therapy. Phys Med 2024; 118:103208. [PMID: 38211462 DOI: 10.1016/j.ejmp.2024.103208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/28/2023] [Accepted: 01/04/2024] [Indexed: 01/13/2024] Open
Abstract
PURPOSE Machine learning (ML) models have been demonstrated to be beneficial for optimizing the workload of patient-specific quality assurance (PSQA). Implementing them in clinical routine frequently requires third-party applications beyond the treatment planning system (TPS), slowing down the workflow. To address this issue, a PSQA outcomes predictive model was carefully selected and validated before being fully integrated into the TPS. MATERIALS AND METHODS Nine ML algorithms were evaluated using cross-validation. The learning database was built by calculating complexity metrics (CM) and binarizing PSQA results into "pass"/"fail" classes for 1767 VMAT arcs. The predictive performance was evaluated using area under the ROC curve (AUROC), sensitivity, and specificity. The ML model was integrated into the TPS via a C# script. Script-guided reoptimization impact on PSQA and dosimetric results was evaluated on ten VMAT plans with "fail"-predicted arcs. Workload reduction potential was also assessed. RESULTS The selected model exhibited an AUROC of 0.88, with a sensitivity and specificity exceeding 50 % and 90 %, respectively. The script-guided reoptimization of the ten evaluated plans led to an average improvement of 1.4 ± 0.9 percentage points in PSQA results, while preserving the quality of the dose distribution. A yearly savings of about 140 h with the use of the script was estimated. CONCLUSIONS The proposed script is a valuable complementary tool for PSQA measurement. It was efficiently integrated into the clinical workflow to enhance PSQA outcomes and reduce PSQA workload by decreasing the risk of failing QA and thereby, the need for repeated replanning and measurements.
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Affiliation(s)
- Caroline Noblet
- Department of Medical Physics, Clinique Mutualiste de l'Estuaire, Cité Sanitaire, Saint-Nazaire, France.
| | - Mathis Maunet
- Department of Medical Physics, Clinique Mutualiste de l'Estuaire, Cité Sanitaire, Saint-Nazaire, France
| | - Marie Duthy
- Department of Medical Physics, Clinique Mutualiste de l'Estuaire, Cité Sanitaire, Saint-Nazaire, France
| | - Frédéric Coste
- Department of Medical Physics, Clinique Mutualiste de l'Estuaire, Cité Sanitaire, Saint-Nazaire, France
| | - Matthieu Moreau
- Department of Medical Physics, Clinique Mutualiste de l'Estuaire, Cité Sanitaire, Saint-Nazaire, France
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Sun W, Mo Z, Li Y, Xiao J, Jia L, Huang S, Liao C, Du J, He S, Chen L, Zhang W, Yang X. Machine learning-based ensemble prediction model for the gamma passing rate of VMAT-SBRT plan. Phys Med 2024; 117:103204. [PMID: 38154373 DOI: 10.1016/j.ejmp.2023.103204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/29/2023] [Accepted: 12/21/2023] [Indexed: 12/30/2023] Open
Abstract
PURPOSE The purpose of this study was to accurately predict or classify the beam GPR with an ensemble model by using machine learning for SBRT(VMAT) plans. METHODS A total of 128 SBRT VMAT plans with 330 arc beams were retrospectively selected, and 216 radiomics and 34 plan complexity features were calculated for each arc beam. Three models for GPR prediction and classification using support vector machine algorithm were as follows: (1) plan complexity feature-based model (plan model); (2) radiomics feature-based model (radiomics model); and (3) an ensemble model combining the two models (ensemble model). The prediction performance was evaluated by calculating the mean absolute error (MAE), root mean square error (RMSE), and Spearman's correlation coefficient (SC), and the classification performance was measured by calculating the area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity. RESULTS The MAE, RMSE and SC at the 2 %/2 mm gamma criterion in the test dataset were 1.4 %, 2.57 %, and 0.563, respectively, for the plan model; 1.42 %, and 2.51 %, and 0.508, respectively, for the radiomics model; and 1.33 %, 2.49 %, and 0.611, respectively, for the ensemble model. The accuracy, specificity, sensitivity, and AUC at the 2 %/2 mm gamma criterion in the test dataset were 0.807, 0.824, 0.681, and 0.854, respectively, for the plan model; 0.860, 0.893, 0.624, and 0.877, respectively, for the radiomics model; and 0.852, 0.871, 0.710, and 0.896, respectively, for the ensemble model. CONCLUSIONS The ensemble model can improve the prediction and classification performance for the GPR of SBRT (VMAT).
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Affiliation(s)
- Wenzhao Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China; Guangdong Esophageal Cancer Institute, Guangzhou, China.
| | - Zijie Mo
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - Yongbao Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jifeng Xiao
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - Lecheng Jia
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China
| | - Sijuan Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Can Liao
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Jinlong Du
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Shumeng He
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Li Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wei Zhang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Xin Yang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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Duan L, Qi W, Chen Y, Cao L, Chen J, Zhang Y, Xu C. Evaluation of complexity and deliverability of IMRT treatment plans for breast cancer. Sci Rep 2023; 13:21474. [PMID: 38052915 PMCID: PMC10698170 DOI: 10.1038/s41598-023-48331-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 11/25/2023] [Indexed: 12/07/2023] Open
Abstract
This study aimed to predict the outcome of patient specific quality assurance (PSQA) in IMRT for breast cancer using complexity metrics, such as MU factor, MAD, CAS, MCS. Several breast cancer plans were considered, including LBCS, RBCS, LBCM, RBCM, left breast, right breast and the whole breast for both Edge and TrueBeam LINACS. Dose verification was completed by Portal Dosimetry (PD). The receiver operating characteristic (ROC) curve was employed to determine whether the treatment plans pass or failed. The area under the curve (AUC) was used to assess the classification performance. The correlation of PSQA and complexity metrics was examined using Spearman's rank correlation coefficient (Rs). For LINACS, the most suitable complexity metric was found to be the MU factor (Edge Rs = - 0.608, p < 0.01; TrueBeam Rs = - 0.739, p < 0.01). Regarding the specific breast cancer categories, the optimal complexity metrics were as follows: MAD (AUC = 0.917) for LBCS, MCS (AUC = 0.681) for RBCS, MU factor (AUC = 0.854) for LBCM and MAD (AUC = 0.731) for RBCM. On the Edge LINAC, the preferable method for breast cancers was MCS (left breast, AUC = 0.938; right breast, AUC = 0.813), while on the TrueBeam LINAC, it became MU factor (left breast, AUC = 0.950) and MCS (right breast, AUC = 0.806), respectively. Overall, there was no universally suitable complexity metric for all types of breast cancers. The choice of complexity metric depended on different cancer types, locations and treatment LINACs. Therefore, when utilizing complexity metrics to predict PSQA outcomes in IMRT for breast cancer, it was essential to select the appropriate metric based on the specific circumstances and characteristics of the treatment.
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Affiliation(s)
- Longyan Duan
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Weixiang Qi
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yi Chen
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lu Cao
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jiayi Chen
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yibin Zhang
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Cheng Xu
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Bin S, Zhang J, Shen L, Zhang J, Wang Q. Study of the prediction of gamma passing rate in dosimetric verification of intensity-modulated radiotherapy using machine learning models based on plan complexity. Front Oncol 2023; 13:1094927. [PMID: 37546404 PMCID: PMC10401596 DOI: 10.3389/fonc.2023.1094927] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 07/04/2023] [Indexed: 08/08/2023] Open
Abstract
Objective To predict the gamma passing rate (GPR) in dosimetric verification of intensity-modulated radiotherapy (IMRT) using three machine learning models based on plan complexity and find the best prediction model by comparing and evaluating the prediction ability of the regression and classification models of three classical algorithms: artificial neural network (ANN), support vector machine (SVM) and random forest (RF). Materials and methods 269 clinical IMRT plans were chosen retrospectively and the GPRs of a total of 2340 fields by the 2%/2mm standard at the threshold of 10% were collected for dosimetric verification using electronic portal imaging device (EPID). Subsequently, the plan complexity feature values of each field were extracted and calculated, and a total of 6 machine learning models (classification and regression models for three algorithms) were trained to learn the relation between 21 plan complexity features and GPRs. Each model was optimized by tuning the hyperparameters and ten-fold cross validation. Finally, the GPRs predicted by the model were compared with measured values to verify the accuracy of the model, and the evaluation indicators were applied to evaluate each model to find the algorithm with the best prediction performance. Results The RF algorithm had the optimal prediction effect on GPR, and its mean absolute error (MAE) on the test set was 1.81%, root mean squared error (RMSE) was 2.14%, and correlation coefficient (CC) was 0.72; SVM was the second and ANN was the worst. Among the classification models, the RF algorithm also had the optimal prediction performance with the highest area under the curve (AUC) value of 0.80, specificity and sensitivity of 0.80 and 0.68 respectively, followed by SVM and the worst ANN. Conclusion All the three classic algorithms, ANN, SVM, and RF, could realize the prediction and classification of GPR. The RF model based on plan complexity had the optimal prediction performance which could save valuable time for quality control workers to improve quality control efficiency.
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Affiliation(s)
- Shizhen Bin
- Radiotherapy Center, Third Xiangya Hospital of Central South University, Changsha, China
| | - Ji Zhang
- Radiotherapy Center, Third Xiangya Hospital of Central South University, Changsha, China
| | - Luyao Shen
- Radiotherapy Center, The Central Hospital of Shaoyang, Shaoyang, China
| | - Junjun Zhang
- Radiotherapy Center, Third Xiangya Hospital of Central South University, Changsha, China
| | - Qi Wang
- Radiotherapy Center, Third Xiangya Hospital of Central South University, Changsha, China
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Han C, Zhang J, Yu B, Zheng H, Wu Y, Lin Z, Ning B, Yi J, Xie C, Jin X. Integrating plan complexity and dosiomics features with deep learning in patient-specific quality assurance for volumetric modulated arc therapy. Radiat Oncol 2023; 18:116. [PMID: 37434171 DOI: 10.1186/s13014-023-02311-7] [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: 01/26/2023] [Accepted: 06/30/2023] [Indexed: 07/13/2023] Open
Abstract
PURPOSE To investigate the feasibility and performance of deep learning (DL) models combined with plan complexity (PC) and dosiomics features in the patient-specific quality assurance (PSQA) for patients underwent volumetric modulated arc therapy (VMAT). METHODS Total of 201 VMAT plans with measured PSQA results were retrospectively enrolled and divided into training and testing sets randomly at 7:3. PC metrics were calculated using house-built algorithm based on Matlab. Dosiomics features were extracted and selected using Random Forest (RF) from planning target volume (PTV) and overlap regions with 3D dose distributions. The top 50 dosiomics and 5 PC features were selected based on feature importance screening. A DL DenseNet was adapted and trained for the PSQA prediction. RESULTS The measured average gamma passing rate (GPR) of these VMAT plans was 97.94% ± 1.87%, 94.33% ± 3.22%, and 87.27% ± 4.81% at the criteria of 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. Models with PC features alone demonstrated the lowest area under curve (AUC). The AUC and sensitivity of PC and dosiomics (D) combined model at 2%/2 mm were 0.915 and 0.833, respectively. The AUCs of DL models were improved from 0.943, 0.849, 0.841 to 0.948, 0.890, 0.942 in the combined models (PC + D + DL) at 3%/3 mm, 3%/2 mm and 2%/2 mm, respectively. A best AUC of 0.942 with a sensitivity, specificity and accuracy of 100%, 81.8%, and 83.6% was achieved with combined model (PC + D + DL) at 2%/2 mm. CONCLUSIONS Integrating DL with dosiomics and PC metrics is promising in the prediction of GPRs in PSQA for patients underwent VMAT.
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Affiliation(s)
- Ce Han
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ji Zhang
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bing Yu
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Haoze Zheng
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yibo Wu
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhixi Lin
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Boda Ning
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jinling Yi
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Congying Xie
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- Department of Medical and Radiation Oncology, 2nd Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Xiance Jin
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- School of Basic Medical Science, Wenzhou Medical University, Wenzhou, China.
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Quintero P, Benoit D, Cheng Y, Moore C, Beavis A. Evaluation of the dataset quality in gamma passing rate predictions using machine learning methods. Br J Radiol 2023:20220302. [PMID: 37129359 DOI: 10.1259/bjr.20220302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023] Open
Abstract
OBJECTIVE Gamma passing rate (GPR) predictions using machine learning methods have been explored for treatment verification of radiotherapy plans. However, these methods presented datasets with unbalanced number of plans having different treatment conditions (heterogeneous datasets), such as anatomical sites or dose per fractions, leading to lower model interpretability and prediction performance. METHODS We investigated the impact of the dataset composition on GPR binary classification (pass/fail) using random forest (RF), XG-boost, and neural network (NN) models. 945 plans were used to create one reference dataset (randomly assembled) and 24 customized datasets that considered four heterogeneity factors independently (anatomical region, number of arcs, dose per fraction, and treatment unit). 309 predictor features were extracted and calculated from plan parameters, modulation complexity metrics, and radiomic analysis (leave-trajectory maps, 3D dose distributions, and portal dosimetry images). The models' performances were measured using the area under the curve from the receiver operating characteristic (ROC-AUC). RESULTS Radiomics features for reference models increased ROC-AUC values up to 13%, 15%, and 5% for RF, XG-Boost, and NN, respectively. The datasets with higher heterogeneous conditions presented the lower ROC-AUC values (RF: 0.72 ± 0.11, XG-Boost: 0.67 ± 0.1, NN: 0.89 ± 0.05) compared to models with less heterogeneous treatment conditions (RF: 0.88 ± 0.06, XG-Boost: 0.89 ± 0.07, NN: 0.98 ± 0.01). The ten most important features for each heterogeneity dataset group demonstrated their correlation with the treatments' physical aspects and GPR prediction. CONCLUSION Improvements in data generalization and model performances can be associated with datasets having similar treatment conditions. This analysis might be implemented to evaluate the dataset quality and model consistency of further ML applications in radiotherapy. ADVANCES IN KNOWLEDGE Dataset heterogeneities decrease ML model performance and reliability.
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Affiliation(s)
- Paulo Quintero
- Faculty of Science and Engineering, University of Hull, Hull, United Kingdom
- Medical Physics Service, Castle Hill Hospital, Hull University Teaching Hospitals NHS Trust, Castle Road, Hull, United Kingdom
| | - David Benoit
- Faculty of Science and Engineering, University of Hull, Hull, United Kingdom
| | - Yongqiang Cheng
- Faculty of Science and Engineering, University of Hull, Hull, United Kingdom
| | - Craig Moore
- Medical Physics Service, Castle Hill Hospital, Hull University Teaching Hospitals NHS Trust, Castle Road, Hull, United Kingdom
| | - Andrew Beavis
- Faculty of Science and Engineering, University of Hull, Hull, United Kingdom
- Medical Physics Service, Castle Hill Hospital, Hull University Teaching Hospitals NHS Trust, Castle Road, Hull, United Kingdom
- Centre for Biomedicine, Hull York Medical School, University of Hull, Hull, United Kingdom
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Lambri N, Hernandez V, Sáez J, Pelizzoli M, Parabicoli S, Tomatis S, Loiacono D, Scorsetti M, Mancosu P. Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process. Phys Med 2023; 110:102593. [PMID: 37104920 DOI: 10.1016/j.ejmp.2023.102593] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/02/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023] Open
Abstract
PURPOSE Patient-specific quality assurance (PSQA) is performed to ensure that modulated treatment plans can be delivered as intended, but constitutes a substantial workload that could slow down the radiotherapy process and delay the start of clinical treatments. In this study, we investigated a machine learning (ML) tree-based ensemble model to predict the gamma passing rate (GPR) for volumetric modulated arc therapy (VMAT) plans. MATERIALS AND METHODS 5622 VMAT plans from multiple treatment sites were selected from a database of Institution 1 and the ML model trained using 19 metrics. PSQA analyses were performed automatically using criteria 3%/1 mm (global normalization, absolute dose, 10% threshold) and 95% action limit. Model's performance was evaluated on an out-of-sample test set of Institution 1 and on two independent sets of measurements collected at Institution 2 and Institution 3. Mean absolute error (MAE), as well as the model's sensitivity and specificity, were computed. RESULTS The model obtained a MAE of 2.33%, 2.54% and 3.91% for the three Institutions, with a specificity of 0.90, 0.90 and 0.68, and a sensitivity of 0.61, 0.25, and 0.55, respectively. Small positive median values of the residuals (i.e., the difference between measurements and predictions) were observed for each Institution (0.95%, 1.66%, and 3.42%). Thus, the model's predictions were, on average, close to the real values and provided a conservative estimation of the GPR. CONCLUSIONS ML models can be integrated into clinical practice to streamline the radiotherapy workflow, but they should be center-specific or thoroughly verified within centers before clinical use.
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Affiliation(s)
- Nicola Lambri
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy; Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy
| | - Victor Hernandez
- Department of Medical Physics, Hospital Universitari Sant Joan de Reus, IISPV, Tarragona, Spain
| | - Jordi Sáez
- Department of Radiation Oncology, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Marco Pelizzoli
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy; Dipartimento di Fisica "Aldo Pontremoli", Università degli Studi di Milano, Milan, Italy
| | - Sara Parabicoli
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy; Dipartimento di Fisica "Aldo Pontremoli", Università degli Studi di Milano, Milan, Italy
| | - Stefano Tomatis
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Daniele Loiacono
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Marta Scorsetti
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy; Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy
| | - Pietro Mancosu
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy.
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Uher K, Ehrbar S, Tanadini-Lang S, Dal Bello R. Reduction of patient specific quality assurance through plan complexity metrics for VMAT plans with an open-source TPS script. Z Med Phys 2023:S0939-3889(23)00011-9. [DOI: 10.1016/j.zemedi.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 01/17/2023] [Accepted: 02/09/2023] [Indexed: 03/31/2023]
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Zhu H, Zhu Q, Wang Z, Yang B, Zhang W, Qiu J. Patient-specific quality assurance prediction models based on machine learning for novel dual-layered MLC linac. Med Phys 2023; 50:1205-1214. [PMID: 36342293 DOI: 10.1002/mp.16091] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 10/12/2022] [Accepted: 10/27/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Patient-specific quality assurance (PSQA) is an indispensable and essential procedure in radiotherapy workflow, and several studies have been done to develop prediction models based on the conventional C-arm linac of single-layered multileaf collimator (MLC) with machine learning (ML) and deep learning techniques to predict PSQA results and improve efficiency. Recently, a newly designed O-ring gantry linac "Halcyon" equipped with unique jawless stacked-and-staggered dual-layered MLC was released. However, few studies have focused on developing PSQA prediction models for this novel dual-layered MLC system. PURPOSE To evaluate the performance of ML to predict PSQA results of fixed field intensity-modulated radiation therapy (FF-IMRT) plans for linac equipped with dual-layered MLC. METHODS AND MATERIALS A total of 213 FF-IMRT treatment plans, including 1383 beams from various treatment sites, were selected and delivered with portal dosimetry verification on Halcyon linac. Gamma analysis was performed using 1%/1, 2%/2, and 3%/2 mm criteria with a 10% threshold. The training set (TS) of ML models consisted of 1106 beams, and an independent evaluation set (ES) consisted of 277 beams. For each beam, 33 complexity metrics were extracted as input data for training models. Three ML algorithms (gradient boosting decision tree/GBDT, random forest/RF, and Poisson Lasso/PL) were utilized to build the models and predict gamma passing rates (GPRs). To improve the prediction accuracy in the rare region, a method of reweighting for TS has been performed and compared to the unweighted results. The importance of complexity metrics was studied by permuted interesting features. RESULTS The GBDT model had the best performance in this study. In ES, the minimal mean prediction error for unweighted results was 1.93%, 1.16%, 0.78% under 1%/1, 2%/2, and 3%/2 mm criteria, respectively, from GBDT model. Comparing to the unweighted results, the models after reweighting gained up to 30% improvement in the rare region, whereas the overall prediction error was slightly worse depending on the kind of models. For feature importance, 2 tree-based models (GBDT and RF) had in common the top 10 most important metrics as well as the same metric with the largest impact. CONCLUSION For linac equipped with novel dual-layered MLC, the ML model based on GBDT algorithm shows a certain degree of accuracy for GPRs prediction. The specific ML model for dual-layered MLC configuration could be a useful tool for physicists detecting PSQA measurement failures.
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Affiliation(s)
- Heling Zhu
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qizhen Zhu
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhiqun Wang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Yang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenjun Zhang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie Qiu
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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A measurement validation of improved plan deliverability with monitor unit objective tool for spine stereotactic ablative radiotherapy. Med Dosim 2022; 48:25-30. [PMID: 36280549 DOI: 10.1016/j.meddos.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 08/25/2022] [Accepted: 09/20/2022] [Indexed: 02/04/2023]
Abstract
Spine stereotactic body radiation therapy (SBRT) uses high dose per fraction for palliative pain control. The treatment plans are often heavily modulated due to close proximity to spinal cord and this can lead to poor plan quality which are susceptible to dose delivery discrepancy. Therefore, we aim to assess the effectiveness of the monitor unit (MU) objective tool in Eclipse treatment planning systems in modulating the plan complexity to improve the plan quality in spine SBRT. Seven retrospective spine SBRT plans are re-optimized using the MU objective tool in Eclipse TPS v13.6 and were compared with the original plans. The dose metrics of the tumor PTV were compared using D1cc. D99%, D95%, D0.03cc, D0.1cc, D0.35cc and D1cc, and that of cord PRV were compared using D0.03cc, D0.1cc, D0.35cc. Four different plan complexities were also calculated for the original and re-optimized plans to quantify the impact of the tool on the modulation. Patient specific quality assurance measurements were performed with Stereophan and SRS MapCheck, and analyzed using the 1%/1-mm and 2%/2-mm criteria with gamma analysis. The dose metrics of the PTV and cord PRV of the re-optimized and original plans are similar and still meet the planning dose constraints. In particular, the PTV dose coverage has a small percentage difference of (0.15 ± 1.33)% and (0.01 ± 1.04)% for D99% and D95%, respectively. The 4 calculated plan complexity metrics consistently show that the re-optimized plans are quantitatively less complex than the original plan. The gamma passing rate of the re-optimized plans improved from (92.2 ± 2.0)% to (94.2 ± 1.6)% with the 1%/1-mm criterion, and (98.7 ± 1.0)% to (99.5 ± 0.3)% with the 2%/2-mm criterion. Overall, the re-optimized plans achieve at least a 10% MU reduction (11.7% to 24.6%). Our study shows that optimization with the MU objective tool can reduce plan complexity and improves dose delivery accuracy, while not compromising the dose distribution.
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Kaplan LP, Placidi L, Bäck A, Canters R, Hussein M, Vaniqui A, Fusella M, Piotrowski T, Hernandez V, Jornet N, Hansen CR, Widesott L. Plan quality assessment in clinical practice: Results of the 2020 ESTRO survey on plan complexity and robustness. Radiother Oncol 2022; 173:254-261. [PMID: 35714808 DOI: 10.1016/j.radonc.2022.06.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 05/24/2022] [Accepted: 06/07/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE Plan complexity and robustness are two essential aspects of treatment plan quality but there is a great variability in their management in clinical practice. This study reports the results of the 2020 ESTRO survey on plan complexity and robustness to identify needs and guide future discussions and consensus. METHODS A survey was distributed online to ESTRO members. Plan complexity was defined as the modulation of machine parameters and increased uncertainty in dose calculation and delivery. Robustness was defined as a dose distribution's sensitivity towards errors stemming from treatment uncertainties, patient setup, or anatomical changes. RESULTS A total of 126 radiotherapy centres from 33 countries participated, 95 of them (75%) from Europe and Central Asia. The majority controlled and evaluated plan complexity using monitor units (56 centres) and aperture shapes (38 centres). To control robustness, 98 (97% of question responses) photon and 5 (50%) proton centres used PTV margins for plan optimization while 75 (94%) and 5 (50%), respectively, used margins for plan evaluation. Seventeen (21%) photon and 8 (80%) proton centres used robust optimisation, while 10 (13%) and 8 (80%), respectively, used robust evaluation. Primary uncertainties considered were patient setup (photons and protons) and range calculation uncertainties (protons). Participants expressed the need for improved commercial tools to control and evaluate plan complexity and robustness. CONCLUSION Clinical implementation of methods to control and evaluate plan complexity and robustness is very heterogeneous. Better tools are needed to manage complexity and robustness in treatment planning systems. International guidelines may promote harmonization.
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Affiliation(s)
- Laura Patricia Kaplan
- Department of Oncology, Aarhus University Hospital, Denmark; Department of Clinical Medicine, Aarhus University, Denmark.
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario ''A. Gemelli'' IRCCS, Roma, Italy.
| | - Anna Bäck
- Department of Therapeutic Radiation Physics, Sahlgrenska University Hospital, Gothenburg, Sweden; Department of Medical Radiation Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Richard Canters
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, the Netherlands
| | - Mohammad Hussein
- Metrology for Med Phys Centre, National Physical Laboratory, Teddington, United Kingdom
| | - Ana Vaniqui
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, the Netherlands
| | - Marco Fusella
- Department of Med Phys, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
| | - Tomasz Piotrowski
- Department of Electroradiology, Poznan University of Medical Sciences and Department of Med Phys, Greater Poland Cancer Centre, Poznan, Poland
| | - Victor Hernandez
- Department of Med Phys, Hospital Sant Joan de Reus, IISPV, Spain
| | - Nuria Jornet
- Servei de Radiofísica i Radioprotecció, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Christian Rønn Hansen
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark
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Wall PDH, Hirata E, Morin O, Valdes G, Witztum A. Prospective clinical validation of virtual patient-specific quality assurance of VMAT radiation therapy plans. Int J Radiat Oncol Biol Phys 2022; 113:1091-1102. [PMID: 35533908 DOI: 10.1016/j.ijrobp.2022.04.040] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 04/05/2022] [Accepted: 04/27/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE Performing measurement-based patient-specific quality assurance (PSQA) is recognized as a resource-intensive and time inefficient task in the radiotherapy treatment workflow. Paired with technological refinements in modern radiotherapy, research towards measurement-free PSQA has seen increased interest over the last five years. However, these efforts have not been clinically implemented or prospectively validated in the U.S. We propose a virtual QA (VQA) system and workflow to assess the safety and workload reduction of measurement-free PSQA. METHODS An XGBoost machine learning model was designed to predict PSQA outcomes of VMAT plans, represented as percent differences between the measured ion chamber point dose in a phantom and the corresponding planned dose. The final model was deployed within a web application to predict PSQA outcomes of clinical plans within an existing clinical workflow. The application also displays relevant feature importance and plan-specific distribution analyses relative to database plans for documentation and to aid physicist interpretation and evaluation. VQA predictions were prospectively validated over three months of measurements at our clinic to assess safety and efficiency gains. RESULTS Over three months, VQA predictions for 445 VMAT plans were prospectively validated at our institution. VQA predictions for these plans had a mean absolute error of 1.08 +/- 0.77%, with a maximum absolute error of 2.98%. Employing a 1% prediction threshold (i.e. plans predicted to have an absolute error of less than 1% would not require a measurement) would yield a 69.2% reduction in QA workload - saving 32.5 hours per month on average - with 81.5%/72.4%/0.81 sensitivity/specificity/AUC at a 3% clinical threshold and 100%/70%/0.93 sensitivity/specificity/AUC at a 4% clinical threshold. CONCLUSION This is the first prospective clinical implementation and validation of VQA in the U.S., which we observed to be efficient. Using a conservative threshold, VQA can substantially reduce the number of required measurements for PSQA, leading to more effective allocation of clinical resources.
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Affiliation(s)
- Phillip D H Wall
- Department of Radiation Oncology, University of California, San Francisco, USA.
| | - Emily Hirata
- Department of Radiation Oncology, University of California, San Francisco, USA
| | - Olivier Morin
- Department of Radiation Oncology, University of California, San Francisco, USA
| | - Gilmer Valdes
- Department of Radiation Oncology, University of California, San Francisco, USA
| | - Alon Witztum
- Department of Radiation Oncology, University of California, San Francisco, USA
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Uncertainty-guided man-machine integrated patient-specific quality assurance. Radiother Oncol 2022; 173:1-9. [DOI: 10.1016/j.radonc.2022.05.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 01/22/2023]
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Noblet C, Duthy M, Coste F, Saliou M, Samain B, Drouet F, Papazyan T, Moreau M. Implementation of volumetric-modulated arc therapy for locally advanced breast cancer patients: Dosimetric comparison with deliverability consideration of planning techniques and predictions of patient-specific QA results via supervised machine learning. Phys Med 2022; 96:18-31. [DOI: 10.1016/j.ejmp.2022.02.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/10/2022] [Accepted: 02/15/2022] [Indexed: 12/21/2022] Open
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Jurado-Bruggeman D, Muñoz-Montplet C, Hernandez V, Saez J, Fuentes-Raspall R. Impact of the dose quantity used in MV photon optimization on dose distribution, robustness, and complexity. Med Phys 2021; 49:648-665. [PMID: 34855988 DOI: 10.1002/mp.15389] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 10/09/2021] [Accepted: 11/18/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Convolution/superposition algorithms used in megavoltage (MV) photon radiotherapy model radiation transport in water, yielding dose to water-in-water (Dw,w ). Advanced algorithms constitute a step forward, but their dose distributions in terms of dose to medium-in-medium (Dm,m ) or dose to water-in-medium (Dw,m ) can be problematic when used in plan optimization due to their different dose responses to some atomic composition heterogeneities. Failure to take this into account can lead to undesired overcorrections and thus to unnoticed suboptimal and unrobust plans. Dose to reference-like medium (Dref,m* ) was recently introduced to overcome these limitations while ensuring accurate transport. This work evaluates and compares the performance of these four dose quantities in planning target volume (PTV)-based optimization. METHODS We considered three cases with heterogeneities inside the PTV: virtual phantom with water surrounded by bone; head and neck; and lung. These cases were planned with volumetric modulated arc therapy (VMAT) technique, optimizing with the same setup and objectives for each dose quantity. We used different algorithms of the Varian Eclipse treatment planning system (TPS): Acuros XB (AXB) for Dm,m and Dw,m , and Analytical Anisotropic Algorithm (AAA) for Dw,w . Dref,m* was obtained from Dm,m distributions using an in-house software considering water as the reference medium (Dw,m* ). The optimization process consisted of: (1) common first optimization, (2) dose distribution computed for each quantity, (3) re-optimization, and (4) final calculation for each dose quantity. The dose distribution, robustness to patient setup errors, and complexity of the plans were analyzed and compared. RESULTS The quantities showed similar dose distributions after the optimization but differed in terms of plan robustness. The cases with soft tissue and high-density heterogeneities followed the same pattern. For AXB Dm,m , cold regions appeared in the heterogeneities after the first optimization. They were compensated in the second optimization through local fluence increases, but any positional mismatch impacted robustness, with clinical target volume (CTV) variations from the nominal scenario around +3% for bone and up to +7% for metal. For AXB Dw,m the pattern was inverse (hot regions compensated by fluence decreases) and more pronounced, with CTV dose variations around -7% for bone and up to -17% for metal. Neither AXB Dw,m* nor AAA Dw,w presented these dose inhomogeneities, which resulted in more robust plans. However, Dw,w differed markedly from the other quantities in the lung case because of its lower radiation transport accuracy. AXB Dm,m was the most complex of the four dose quantities and AXB Dw,m* the least complex, though we observed no major differences in this regard. CONCLUSIONS The dose quantity used in MV photon optimization can affect plan robustness. Dw,w distributions from convolution/superposition algorithms are robust but may not provide sufficient radiation transport accuracy in some cases. Dm,m and Dw,m from advanced algorithms can compromise robustness because their different responses to some composition heterogeneities introduce additional fluence compensations. Dref,m* offers advantages in plan optimization and evaluation, producing accurate and robust plans without increasing complexity. Dref,m* can be easily implemented as a built-in feature of the TPS and can facilitate and simplify the treatment planning process when using advanced algorithms. Final reporting can be kept in Dm,m or Dw,m for clinical correlations.
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Affiliation(s)
- Diego Jurado-Bruggeman
- Medical Physics and Radiation Protection Department, Institut Català d'Oncologia, Girona, Spain
| | - Carles Muñoz-Montplet
- Medical Physics and Radiation Protection Department, Institut Català d'Oncologia, Girona, Spain.,Department of Medical Sciences, University of Girona, Girona, Spain
| | - Victor Hernandez
- Department of Medical Physics, Hospital Universitari Sant Joan de Reus, IISPV, Tarragona, Spain.,Universitat Rovira i Virgili, Tarragona, Spain
| | - Jordi Saez
- Department of Radiation Oncology, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Rafael Fuentes-Raspall
- Department of Medical Sciences, University of Girona, Girona, Spain.,Radiation Oncology Department, Institut Català d'Oncologia, Girona, Spain
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The impact of different optimization strategies on the agreement between planned and delivered doses during volumetric modulated arc therapy for total marrow irradiation. Contemp Oncol (Pozn) 2021; 25:100-106. [PMID: 34667436 PMCID: PMC8506427 DOI: 10.5114/wo.2021.107742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 05/13/2021] [Indexed: 11/17/2022] Open
Abstract
Aim of the study To evaluate the agreement between planned and delivered doses and its potential correlation with the plans' complexity subjected to dosimetric verification. Material and methods Four isocentre volumetric modulated arc therapy for total marrow irradiation plans optimized simultaneously with (P1) and without (P2) MU reduction were evaluated dosimetrically by γ method performed in a global mode for 4 combinations of γ-index criteria (2%/2 mm, 2%/3 mm, 3%/2 mm, and 3%/3 mm). The evaluation was conducted for 4 regions (head and neck, chest, abdomen and upper pelvis, and lower pelvis and thighs) that were determined geometrically by the isocentres. The Wilcoxon test was used to detect significant differences between γ passing rate (GPR) analysis results for the P1 and P2 plans. The Pearson correlation was used to check the relationship between GPR and the plans' complexity. Results Except for the head and neck region, the P2 plans had better GPRs than the P1 plans. Only for hard combinations of γ-index criteria (i.e. 2%/3 mm, 2%/2 mm) were the GPRs differences between P1 and P2 clinically meaningful, and they were detected in the chest, abdomen and upper pelvis, and lower pelvis and thighs regions. The highest correlations between GPR and the indices describing the plans' complexity were found for the chest region. No correlation was found for the head and neck region. Conclusions The P2 plans showed better agreement between planned and delivered doses compared to the P1 plans. The GPR and the plans' complexity depend on the anatomy region and are most important for the chest region.
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Tambe NS, Pires IM, Moore C, Wieczorek A, Upadhyay S, Beavis AW. Predicting personalised optimal arc parameter using knowledge-based planning model for inoperable locally advanced lung cancer patients to reduce organ at risk doses. Biomed Phys Eng Express 2021; 7. [PMID: 34517350 DOI: 10.1088/2057-1976/ac2635] [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: 06/18/2021] [Accepted: 09/13/2021] [Indexed: 11/12/2022]
Abstract
Objectives. Volumetric modulated arc therapy (VMAT) allows for reduction of organs at risk (OAR) volumes receiving higher doses, but increases OAR volumes receiving lower radiation doses and can subsequently increasing associated toxicity. Therefore, reduction of this low-dose-bath is crucial. This study investigates personalizing the optimization of VMAT arc parameters (gantry start and stop angles) to decrease OAR doses.Materials and Methods. Twenty previously treated locally advanced non-small cell lung cancer (NSCLC) patients treated with half-arcs were randomly selected from our database. These plans were re-optimized with seven different arcs parameters; optimization objectives were kept constant for all plans. All resulting plans were reviewed by two clinicians and the optimal plan (lowest OAR doses and adequate target coverage) was selected. Furthermore, knowledge-based planning (KBP) model was developed using these plans as 'training data' to predict optimal arc parameters for individual patients based on their anatomy. Treatment plan complexity scores and deliverability measurements were performed for both optimal and original clinical plans.Results.The results show that different arc geometries resulted in different dose distributions to the OAR but target coverage was mostly similar. Different arc geometries were required for different patients to minimize OAR doses. Comparison of the personalized against the standard (2 half-arcs) plans showed a significant reduction in lung V5(lung volume receiving 5 Gy), mean lung dose and mean heart doses. Reduction in lung V20and heart V30were statistically insignificant. Plan complexity and deliverability measurements show the test plans can be delivered as planned.Conclusions.Our study demonstrated that personalizing arc parameters based on an individual patient's anatomy significantly reduces both lung and heart doses. Dose reduction is expected to reduce toxicity and improve the quality of life for these patients.
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Affiliation(s)
- Nilesh S Tambe
- Radiotherapy Physics, Queen's Centre for Oncology, Hull University Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, United Kingdom.,Faculty of Health Sciences, University of Hull, Cottingham road, Hull, HU16 7RX, United Kingdom
| | - Isabel M Pires
- Faculty of Health Sciences, University of Hull, Cottingham road, Hull, HU16 7RX, United Kingdom
| | - Craig Moore
- Radiotherapy Physics, Queen's Centre for Oncology, Hull University Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, United Kingdom
| | - Andrew Wieczorek
- Clinical Oncology, Queen's Centre for Oncology, Hull University Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, United Kingdom
| | - Sunil Upadhyay
- Clinical Oncology, Queen's Centre for Oncology, Hull University Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, United Kingdom
| | - Andrew W Beavis
- Radiotherapy Physics, Queen's Centre for Oncology, Hull University Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, United Kingdom.,Faculty of Health Sciences, University of Hull, Cottingham road, Hull, HU16 7RX, United Kingdom.,Faculty of Health and Well Being, Sheffield-Hallam University, Collegiate Crescent, Sheffield, S10 2BP, United Kingdom
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Evaluation of treatment plan quality for head and neck IMRT: a multicenter study. Med Dosim 2021; 46:310-317. [PMID: 33838998 DOI: 10.1016/j.meddos.2021.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 01/06/2021] [Accepted: 03/05/2021] [Indexed: 11/23/2022]
Abstract
Intensity-modulated radiotherapy (IMRT) treatment planning for head and neck cancer is challenging and complex due to many organs at risk (OAR) in this region. The experience and skills of planners may result in substantial variability of treatment plan quality. This study assessed the performance of IMRT planning in Malaysia and observed plan quality variation among participating centers. The computed tomography dataset containing contoured target volumes and OAR was provided to participating centers. This is to control variations in contouring the target volumes and OARs by oncologists. The planner at each center was instructed to complete the treatment plan based on clinical practice with a given prescription, and the plan was analyzed against the planning goals provided. The quality of completed treatment plans was analyzed using the plan quality index (PQI), in which a score of 0 indicated that all dose objectives and constraints were achieved. A total of 23 plans were received from all participating centers comprising 14 VMAT, 7 IMRT, and 2 tomotherapy plans. The PQI indexes of these plans ranged from 0 to 0.65, indicating a wide variation of plan quality nationwide. Results also reported 5 out of 21 plans achieved all dose objectives and constraints showing more professional training is needed for planners in Malaysia. Understanding of treatment planning system and computational physics could also help in improving the quality of treatment plans for IMRT delivery.
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Wall PDH, Fontenot JD. Quality assurance-based optimization (QAO): Towards improving patient-specific quality assurance in volumetric modulated arc therapy plans using machine learning. Phys Med 2021; 87:136-143. [PMID: 33775567 DOI: 10.1016/j.ejmp.2021.03.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 03/01/2021] [Accepted: 03/09/2021] [Indexed: 10/21/2022] Open
Abstract
INTRODUCTION Previous literature has shown general trade-offs between plan complexity and resulting quality assurance (QA) outcomes. However, existing solutions for controlling this trade-off do not guarantee corresponding improvements in deliverability. Therefore, this work explored the feasibility of an optimization framework for directly maximizing predicted QA outcomes of plans without compromising the dosimetric quality of plans designed with an established knowledge-based planning (KBP) technique. MATERIALS AND METHODS A support vector machine (SVM) was developed - using a database of 500 previous VMAT plans - to predict gamma passing rates (GPRs; 3%/3mm percent dose-difference/distance-to-agreement with local normalization) based on selected complexity features. A heuristic, QA-based optimization (QAO) framework was devised by utilizing the SVM model to iteratively modify mechanical treatment features most commonly associated with suboptimal GPRs. Specifically, leaf gaps (LGs) <50 mm were widened by random amounts, which impacts all aperture-based complexity features. 13 prostate KBP-guided VMAT plans were optimized via QAO using user-specified maximum LG displacements before corresponding changes in predicted GPRs and dose were assessed. RESULTS Predicted GPRs increased by an average of 1.14 ± 1.25% (p = 0.006) with QAO using a 3 mm maximum random LG displacement. There were small differences in dose, resulting in similarly small changes in tumor control probability (maximum increase = 0.05%) and normal tissue complication probabilities in the bladder, rectum, and femoral heads (maximum decrease = 0.2% in the rectum). CONCLUSION This study explored the feasibility of QAO and warrants future investigations of further incorporating QA endpoints into plan optimization.
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Affiliation(s)
- Phillip D H Wall
- Department of Physics and Astronomy, Louisiana State University and Agricultural and Mechanical College, 202 Tower Drive, Baton Rouge, LA 70803-4001, USA.
| | - Jonas D Fontenot
- Department of Physics and Astronomy, Louisiana State University and Agricultural and Mechanical College, 202 Tower Drive, Baton Rouge, LA 70803-4001, USA; Department of Physics, Mary Bird Perkins Cancer Center, 4950 Essen Lane, Baton Rouge, LA 70809, USA
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22
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Li C, Tao C, Bai T, Li Z, Tong Y, Zhu J, Yin Y, Lu J. Beam complexity and monitor unit efficiency comparison in two different volumetric modulated arc therapy delivery systems using automated planning. BMC Cancer 2021; 21:261. [PMID: 33691654 PMCID: PMC7945217 DOI: 10.1186/s12885-021-07991-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 02/28/2021] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND To investigate the beam complexity and monitor unit (MU) efficiency issues for two different volumetric modulated arc therapy (VMAT) delivery technologies for patients with left-sided breast cancer (BC) and nasopharyngeal carcinoma (NPC). METHODS Twelve left-sided BC and seven NPC cases were enrolled in this study. Each delivered treatment plan was optimized in the Pinnacle3 treatment planning system with the Auto-Planning module for the Trilogy and Synergy systems. Similar planning dose objectives and beam configurations were used for each site in the two different delivery systems to produce clinically acceptable plans. The beam complexity was evaluated in terms of the segment area (SA), segment width (SW), leaf sequence variability (LSV), aperture area variability (AAV), and modulation complexity score (MCS) based on the multileaf collimator sequence and MU. Plan delivery and a gamma evaluation were performed using a helical diode array. RESULTS With similar plan quality, the average SAs for the Trilogy plans were smaller than those for the Synergy plans: 55.5 ± 21.3 cm2 vs. 66.3 ± 17.9 cm2 (p < 0.05) for the NPC cases and 100.7 ± 49.2 cm2 vs. 108.5 ± 42.7 cm2 (p < 0.05) for the BC cases, respectively. The SW was statistically significant for the two delivery systems (NPC: 6.87 ± 1.95 cm vs. 6.72 ± 2.71 cm, p < 0.05; BC: 8.84 ± 2.56 cm vs. 8.09 ± 2.63 cm, p < 0.05). The LSV was significantly smaller for Trilogy (NPC: 0.84 ± 0.033 vs. 0.86 ± 0.033, p < 0.05; BC: 0.89 ± 0.026 vs. 0.90 ± 0.26, p < 0.05). The mean AAV was significantly larger for Trilogy than for Synergy (NPC: 0.18 ± 0.064 vs. 0.14 ± 0.037, p < 0.05; BC: 0.46 ± 0.15 vs. 0.33 ± 0.13, p < 0.05). The MCS values for Trilogy were higher than those for Synergy: 0.14 ± 0.016 vs. 0.12 ± 0.017 (p < 0.05) for the NPC cases and 0.42 ± 0.106 vs. 0.30 ± 0.087 (p < 0.05) for the BC cases. Compared with the Synergy plans, the average MUs for the Trilogy plans were larger: 828.6 ± 74.1 MU and 782.9 ± 85.2 MU (p > 0.05) for the NPC cases and 444.8 ± 61.3 MU and 393.8 ± 75.3 MU (p > 0.05) for the BC cases. The gamma index agreement scores were never below 91% using 3 mm/3% (global) distance to agreement and dose difference criteria and a 10% lower dose exclusion threshold. CONCLUSIONS The Pinnacle3 Auto-Planning system can optimize BC and NPC plans to achieve the same plan quality using both the Trilogy and Synergy systems. We found that these two systems resulted in different SAs, SWs, LSVs, AAVs and MCSs. As a result, we suggested that the beam complexity should be considered in the development of further methodologies while optimizing VMAT autoplanning.
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Affiliation(s)
- Chengqiang Li
- Department of Radiation Oncology Physics, Shandong Cancer Hospital and Institute, Cancer Hospital affiliated to Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Cheng Tao
- Department of Radiation Oncology Physics, Shandong Cancer Hospital and Institute, Cancer Hospital affiliated to Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Tong Bai
- Department of Radiation Oncology Physics, Shandong Cancer Hospital and Institute, Cancer Hospital affiliated to Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Zhenjiang Li
- Department of Radiation Oncology Physics, Shandong Cancer Hospital and Institute, Cancer Hospital affiliated to Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Ying Tong
- Department of Radiation Oncology Physics, Shandong Cancer Hospital and Institute, Cancer Hospital affiliated to Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Jian Zhu
- Department of Radiation Oncology Physics, Shandong Cancer Hospital and Institute, Cancer Hospital affiliated to Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Yong Yin
- Department of Radiation Oncology Physics, Shandong Cancer Hospital and Institute, Cancer Hospital affiliated to Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China.
| | - Jie Lu
- Department of Radiation Oncology Physics, Shandong Cancer Hospital and Institute, Cancer Hospital affiliated to Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China.
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23
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Ito T, Tamura M, Monzen H, Matsumoto K, Nakamatsu K, Harada T, Fukui T. [Impact of Aperture Shape Controller on Knowledge-based VMAT Planning of Prostate Cancer]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2021; 77:23-31. [PMID: 33473076 DOI: 10.6009/jjrt.2021_jsrt_77.1.23] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE Knowledge-based planning (KBP) has disadvantages of high monitor unit (MU) and complex multi-leaf collimator (MLC) motion. We investigated the optimal aperture shape controller (ASC) level for the KBP to reduce these factors in volumetric modulated arc therapy (VMAT) for prostate cancer. METHODS The KBP model was created based on 51 clinical plans (CPs) of patients who underwent the VMAT for prostate cancer. Another 10 CPs were selected randomly, and the KBPs with/without ASC, changed stepwise from very low (KBP-VL) to very high (KBP-VH), were performed with a single auto-optimization. The parameters of dose-volume histograms (DVHs) and MLC performance metrics were evaluated. We obtained the modulation complexity score for VMAT (MCSv), closed leaf score (CLS), small aperture score (SAS), leaf travel (LT), and total MU. RESULTS The ASC did not affect the DVH parameters negatively. The following comparisons of MLC performance were obtained (KBP vs. KBP-VL vs. KBP-VH, respectively): 0.25 vs. 0.27 vs. 0.30 (MCSv), 0.19 vs. 0.18 vs. 0.16 (CLS), 0.50 vs. 0.45 vs. 0.40 (SAS10 mm), 0.73 vs. 0.68 vs. 0.63 (SAS20 mm), 768.35 mm vs. 671.50 mm vs. 551.32 mm (LT), and 672.87 vs. 642.36 vs. 607.59 (MU). There were significant differences between KBP and KBP-VH for MCSv and LT (p<0.05). CONCLUSIONS The KBP using an ASC set to the very high level could reduce the complexity of MLC motion significantly more without deterioration of the DVH parameters compared with the KBP in VMAT for prostate cancer.
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Affiliation(s)
- Takaaki Ito
- Department of Radiological Technology, Kobe City Nishi-Kobe Medical Center
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University
| | - Kenji Matsumoto
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University.,Department of Radiology, Kindai University Hospital
| | - Kiyoshi Nakamatsu
- Department of Radiation Oncology, Faculty of Medicine, Kindai University
| | - Tomoko Harada
- Department of Radiological Technology, Kobe City Nishi-Kobe Medical Center
| | - Tatsuya Fukui
- Department of Radiological Technology, Kobe City Nishi-Kobe Medical Center
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24
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Masi L, Hernandez V, Saez J, Doro R, Livi L. Robotic MLC-based plans: A study of plan complexity. Med Phys 2021; 48:942-952. [PMID: 33332628 DOI: 10.1002/mp.14667] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 11/10/2020] [Accepted: 12/08/2020] [Indexed: 12/19/2022] Open
Abstract
PURPOSE The utility of complexity metrics has been assessed for IMRT and VMAT treatment plans, but this analysis has never been performed for CyberKnife (CK) plans. The purpose of this study is to perform a complexity analysis of CK MLC plans, adapting and computing complexity indices previously defined for IMRT plans. Metrics were used to compare the complexity of plans created by two optimization systems and to study correlations between plan complexity and patient-specific quality assurance (PSQA) results. Relationships between pairs of metrics were also analyzed to get insight into possible interdependencies. METHODS Two independent in-house software platforms were developed to compute six complexity metrics: modulation complexity score (MCS), edge metric (EM), plan irregularity (PI), plan modulation (PM), leaf gap (LG), and small aperture score (SAS10). MCS and PM definitions were adapted to account for CK plans characteristics. The computed metrics were used to compare the existing optimization algorithms (sequential and VOLO) in terms of plan complexity over 24 selected cases. Metrics were then computed over a large number (103) of VOLO SBRT clinical plans from different treatment sites, mainly liver, prostate, pancreas, and spine. Pearson's r was used to study relationships between each pair of metrics. Correlation between complexity indices and PSQA results expressed as gamma index passing rates (GPR) at (3%, 1 mm) and (2%, 1 mm) was finally analyzed. Correlation was regarded as weak for absolute Pearson's r values in the range 0.2-0.39, moderate 0.4-0.59, strong 0.6-0.79, and very strong 0.8-1. RESULTS When compared to VOLO, sequential plans exhibited a higher complexity degree, showing lower MCS and LG values and higher EM, PM and PI values. Differences were significant for 5/6 metrics (Wilcoxon P < 0.05). The analysis of VOLO clinical plans highlighted different degrees of complexity among plans from different treatment sites, increasing from liver to prostate, pancreas, and finally, spine. Analysis of dependencies between pairs of metrics showed a very strong significant negative correlation (P < 0.01), respectively, between MCS and PM (r = -0.97), and EM and LG (-0.82). Most of the remaining pairs showed moderate to strong correlations with the exception of PI, which showed weaker correlations with the other metrics. A moderate significant correlation was observed with GPR values both at (3%, 1 mm) and (2%, 1 mm) for all metrics except PI, which showed no correlation. CONCLUSIONS Modulation complexity metrics were computed for CK MLC-based plans for the first time and some metrics' definitions were adapted to CK plans peculiarities. The computed metrics proved a useful tool for comparing optimization algorithms and for characterizing CK clinical plans. Strong and very strong correlations were found between some pairs of metrics. Some significant correlations were found with PSQA GPR, indicating that some indices are promising for rationalizing and reducing PSQA workload. Our results set the basis for evaluating new optimization algorithms and TPS versions in the future, as well as for comparing the complexity of CK MLC-based plans in multicenter and multiplatform comparisons.
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Affiliation(s)
- Laura Masi
- Department of Medical Physics, Radiation Oncology IFCA, Florence, 50139, Italy
| | - Victor Hernandez
- Department of Medical Physics, Hospital Universitari Sant Joan de Reus, IISPV, Tarragona, 43204, Spain
| | - Jordi Saez
- Department of Radiation Oncology, Hospital Clinic de Barcelona, Barcelona, 08036, Spain
| | - Raffaela Doro
- Department of Medical Physics, Radiation Oncology IFCA, Florence, 50139, Italy
| | - Lorenzo Livi
- Radiotherapy Unit AOU Careggi, Florence, 50139, Italy.,Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, 50139, Italy
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25
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Nguyen M, Chan GH. Quantified VMAT plan complexity in relation to measurement-based quality assurance results. J Appl Clin Med Phys 2020; 21:132-140. [PMID: 33112467 PMCID: PMC7700925 DOI: 10.1002/acm2.13048] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/09/2020] [Accepted: 08/15/2020] [Indexed: 11/16/2022] Open
Abstract
Volumetric‐modulated arc therapy (VMAT) treatment plans that are highly modulated or complex may result in disagreements between the planned dose distribution and the measured dose distribution. This study investigated established VMAT complexity metrics as a means of predicting phantom‐based measurement results for 93 treatments delivered on a TrueBeam linac, and 91 treatments delivered on two TrueBeam STx linacs. Complexity metrics investigated showed weak correlations to gamma passing rate, with the exception of the Modulation Complexity Score for VMAT, yielding moderate correlations. The Spearman’s rho values for this metric were 0.502 (P < 0.001) and 0.528 (P < 0.001) for the TrueBeam and TrueBeam STx, respectively. Receiver operating characteristic analysis was also performed. The aperture irregularity on the TrueBeam achieved a 53% true positive rate and a 9% false‐positive rate to correctly identify complex plans. Similarly, the average field width on the TrueBeam STx achieved a 60% true‐positive rate and an 8% false‐positive rate. If incorporated into clinical workflow, these thresholds can identify highly modulated plans and reduce the number of dose verification measurements required.
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Affiliation(s)
- Michael Nguyen
- Department of Medical Physics, Juravinski Cancer Centre, Hamilton, ON, Canada
| | - Gordon H Chan
- Department of Medical Physics, Juravinski Cancer Centre, Hamilton, ON, Canada
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26
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Kairn T, Livingstone AG, Crowe SB. Monte Carlo calculations of radiotherapy dose in "homogeneous" anatomy. Phys Med 2020; 78:156-165. [PMID: 33035927 DOI: 10.1016/j.ejmp.2020.09.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/05/2020] [Accepted: 09/21/2020] [Indexed: 01/27/2023] Open
Abstract
Given the substantial literature on the use of Monte Carlo (MC) simulations to verify treatment planning system (TPS) calculations of radiotherapy dose in heterogeneous regions, such as head and neck and lung, this study investigated the potential value of running MC simulations of radiotherapy treatments of nominally homogeneous pelvic anatomy. A pre-existing in-house MC job submission and analysis system, built around BEAMnrc and DOSXYZnrc, was used to evaluate the dosimetric accuracy of a sample of 12 pelvic volumetric arc therapy (VMAT) treatments, planned using the Varian Eclipse TPS, where dose was calculated with both the Analytical Anisotropic Algorithm (AAA) and the Acuros (AXB) algorithm. In-house TADA (Treatment And Dose Assessor) software was used to evaluate treatment plan complexity, in terms of the small aperture score (SAS), modulation index (MI) and a novel exposed leaf score (ELS/ELA). Results showed that the TPS generally achieved closer agreement with the MC dose distribution when treatments were planned for smaller (single-organ) targets rather than larger targets that included nodes or metastases. Analysis of these MC results with reference to the complexity metrics indicated that while AXB was useful for reducing dosimetric uncertainties associated with density heterogeneity, the residual TPS dose calculation uncertainties resulted from treatment plan complexity and TPS model simplicity. The results of this study demonstrate the value of using MC methods to recalculate and check the dose calculations provided by commercial radiotherapy TPSs, even when the treated anatomy is assumed to be comparatively homogeneous, such as in the pelvic region.
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Affiliation(s)
- Tanya Kairn
- Royal Brisbane and Women's Hospital, Butterfield Street, Herston, QLD 4029, Australia; Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia.
| | | | - Scott B Crowe
- Royal Brisbane and Women's Hospital, Butterfield Street, Herston, QLD 4029, Australia; Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia
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27
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Hernandez V, Hansen CR, Widesott L, Bäck A, Canters R, Fusella M, Götstedt J, Jurado-Bruggeman D, Mukumoto N, Kaplan LP, Koniarová I, Piotrowski T, Placidi L, Vaniqui A, Jornet N. What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans. Radiother Oncol 2020; 153:26-33. [PMID: 32987045 DOI: 10.1016/j.radonc.2020.09.038] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/17/2020] [Accepted: 09/17/2020] [Indexed: 12/25/2022]
Abstract
Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy.
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Affiliation(s)
- Victor Hernandez
- Department of Medical Physics, Hospital Sant Joan de Reus, IISPV, Spain.
| | - Christian Rønn Hansen
- Laboratory of Radiation Physics, Odense University Hospital, Denmark; Institute of Clinical Research, University of Southern Denmark, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Denmark
| | | | - Anna Bäck
- Department of Therapeutic Radiation Physics, Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden; Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy at the University of Gothenburg, Sweden
| | - Richard Canters
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Marco Fusella
- Medical Physics Department, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | - Julia Götstedt
- Department of Radiation Physics, University of Gothenburg, Göteborg, Sweden
| | - Diego Jurado-Bruggeman
- Medical Physics and Radiation Protection Department, Institut Català d'Oncologia, Girona, Spain
| | - Nobutaka Mukumoto
- Department of Radiation Oncology and Image-applied Therapy, Graduate, School of Medicine, Kyoto University, Japan
| | | | - Irena Koniarová
- National Radiation Protection Institute, Prague, Czech Republic
| | - Tomasz Piotrowski
- Department of Electroradiology, Poznań University of Medical Sciences, Poznań, Poland; Department of Medical Physics, Greater Poland Cancer Centre, Poznań, Poland
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Roma, Italy
| | - Ana Vaniqui
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Nuria Jornet
- Servei de Radiofísica i Radioprotecció, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
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28
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Lobb EC, Degnan M. Comparison of VMAT complexity-reduction strategies for single-target cranial radiosurgery with the Eclipse treatment planning system. J Appl Clin Med Phys 2020; 21:97-108. [PMID: 32920991 PMCID: PMC7592979 DOI: 10.1002/acm2.13014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 07/23/2020] [Accepted: 07/28/2020] [Indexed: 11/11/2022] Open
Abstract
Complexity in MLC‐based radiosurgery treatment delivery can be characterized by the efficiency of monitor unit (MU) utilization and the average MLC leaf separation distance for a treatment plan. A reduction in plan complexity may be desirable if plan quality is not impacted. In this study, a number of strategies are explored to determine how plan quality is affected by efforts to reduce plan complexity. Ten radiosurgery cases of varying complexity are retrospectively planned using six optimization strategies: an unconstrained volumetric modulated arc therapy (VMAT) technique, a MU‐constrained VMAT technique, three techniques using various strengths of the aperture shape controller (ASC), and a hybrid technique consisting of a final‐stage VMAT optimization applied to a dynamic conformal arc leaf sequence (ODCA). The plans are compared in terms of MU efficiency, MLC leaf‐separation, conformity index (CI), gradient index (GI), and QA measurement results. The five VMAT techniques exhibited only minor differences in CI and GI values, though the ASC and MU‐constrained techniques did require 6–20% fewer MU and had mean field apertures 5–19% larger. On average, the ODCA technique had CI values 3.5% lower and GI values 1.0–2.5% higher than the VMAT techniques, but also had a mean field aperture 24–47% larger and required 16–32% fewer MU. The QA measurement results showed a 0.61% variation in mean per‐field 2%/1 mm gamma passing rates across all techniques (range 96.81%–97.42%), with no observed correlation between passing rate and technique. For simple targets, the ODCA technique achieved CI results that were equivalent to the unconstrained VMAT technique with an average 30% reduction in required MU, an average 50% increase in mean leaf separation distance, and brain V12Gy values within 0.38 cc of the VMAT technique for targets up to approximately 2 cm diameter. For MLC‐based single‐target radiosurgery, plan complexity can often be significantly reduced without an equivalent reduction in plan quality.
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Affiliation(s)
- Eric C Lobb
- Department of Radiation Oncology, Ascension NE Wisconsin - St. Elizabeth Hospital, Appleton, WI, USA
| | - Michael Degnan
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
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29
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Tamura M, Matsumoto K, Otsuka M, Monzen H. Plan complexity quantification of dual-layer multi-leaf collimator for volumetric modulated arc therapy with Halcyon linac. Phys Eng Sci Med 2020; 43:947-957. [DOI: 10.1007/s13246-020-00891-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 06/23/2020] [Indexed: 12/31/2022]
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30
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Litoborska J, Piotrowski T, Malicki J. Evaluation of three VMAT-TMI planning methods to find an appropriate balance between plan complexity and the resulting dose distribution. Phys Med 2020; 75:26-32. [PMID: 32480353 DOI: 10.1016/j.ejmp.2020.05.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 05/20/2020] [Accepted: 05/23/2020] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Evaluation of different planning methods of treatment plan preparation for volumetric modulated arc therapy during total marrow irradiation (VMAT-TMI). METHOD Three different planning methods were evaluated to establish the most appropriate VMAT-TMI technique, based on organ at risk (OAR) dose reduction, conformity and plan simplicity. The methods were: (M1) the sub-plan method, (M2) use of eight arcs optimised simultaneously and (M3) M2 with monitor unit reduction. Friedman ANOVA comparison, with Nemenyi's procedures, was used in the statistical analysis of the results. RESULTS The dosimetric results obtained for the planning target volume and for most OARs do not differ statistically between methods. The M3 method was characterized by the lowest numbers of monitor units (3259 MU vs. 4450 MU for M1 and 4216 MU for M2) and, in general, the lowest complexity. The variability of the monitor units from control points was almost half for M3 than M1 and M2 (i.e. 0.33 MU vs. 0.61 MU for M1 and 0.58 for M2). Analysing the relationship between the dose distributions obtained for the plans and their complexity, the best result was observed for the M3 method. CONCLUSION The use of eight simultaneously optimised arcs with MU reduction allows to obtain VMAT-TMI plans that are characterized by the lowest complexity, with dose distributions comparable to the plans generated by other methods.
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Affiliation(s)
- Joanna Litoborska
- Department of Medical Physics, Greater Poland Cancer Centre, Poznań, Poland
| | - Tomasz Piotrowski
- Department of Medical Physics, Greater Poland Cancer Centre, Poznań, Poland; Department of Electroradiology, Poznań University of Medical Sciences, Poznań, Poland.
| | - Julian Malicki
- Department of Medical Physics, Greater Poland Cancer Centre, Poznań, Poland; Department of Electroradiology, Poznań University of Medical Sciences, Poznań, Poland
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31
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Santos T, Ventura T, Mateus J, Capela M, Lopes MDC. On the complexity of helical tomotherapy treatment plans. J Appl Clin Med Phys 2020; 21:107-118. [PMID: 32363800 PMCID: PMC7386195 DOI: 10.1002/acm2.12895] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 04/09/2020] [Accepted: 04/13/2020] [Indexed: 11/08/2022] Open
Abstract
PURPOSE Multiple metrics are proposed to characterize and compare the complexity of helical tomotherapy (HT) plans created for different treatment sites. METHODS A cohort composed of 208 HT plans from head and neck (105), prostate (51) and brain (52) tumor sites was considered. For each plan, 14 complexity metrics were calculated. Those metrics evaluate the percentage of leaves with small opening times or approaching the projection duration, the percentage of closed leaves, the amount of tongue-and-groove effect, and the overall modulation of the planned sinogram. To enable data visualization, an approach based on principal component analysis was followed to reduce the dataset dimensionality. This allowed the calculation of a global plan complexity score. The correlation between plan complexity and pretreatment verification results using the Spearman's rank correlation coefficients was investigated. RESULTS According to the global score, the most complex plans were the head and neck tumor cases, followed by the prostate and brain lesions irradiated with stereotactic technique. For almost all individual metrics, head and neck plans confirmed to be the plans with the highest complexity. Nevertheless, prostate cases had the highest percentage of leaves with an opening time approaching the projection duration, whereas the stereotactic brain plans had the highest percentage of closed leaves per projection. Significant correlations between some of the metrics and the pretreatment verification results were identified for the stereotactic brain group. CONCLUSIONS The proposed metrics and the global score demonstrated to be useful to characterize and quantify the complexity of HT plans of different treatment sites. The reported differences inter- and intra-group may be valuable to guide the planning process aiming at reducing uncertainties and harmonize planning strategies.
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Affiliation(s)
- Tania Santos
- Physics Department, University of Coimbra, Coimbra, Portugal.,Medical Physics Department, IPOCFG, E.P.E, Coimbra, Portugal
| | - Tiago Ventura
- Medical Physics Department, IPOCFG, E.P.E, Coimbra, Portugal
| | - Josefina Mateus
- Medical Physics Department, IPOCFG, E.P.E, Coimbra, Portugal
| | - Miguel Capela
- Medical Physics Department, IPOCFG, E.P.E, Coimbra, Portugal
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32
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Jodda A, Piotrowski T, Kruszyna-Mochalska M, Malicki J. Impact of different optimization strategies on the compatibility between planned and delivered doses during radiation therapy of cervical cancer. Rep Pract Oncol Radiother 2020; 25:412-421. [PMID: 32372881 PMCID: PMC7191125 DOI: 10.1016/j.rpor.2020.03.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 03/13/2020] [Accepted: 03/30/2020] [Indexed: 11/23/2022] Open
Abstract
PURPOSE To analyse the impact of different optimization strategies on the compatibility between planned and delivered doses during radiotherapy of cervical cancer. MATERIAL/METHODS Four treatment plans differing in optimisation strategies were prepared for ten cervical cancer cases. These were: volumetric modulated arc therapy with (_OPT) and without optimization of the doses in the bone marrow and for two sets of margins applied to the clinical target volume that arose from image guidance based on the bones (IG(B)) and soft tissues (IG(ST)). The plans were subjected to dosimetric verification by using the ArcCHECK system and 3DVH software. The planned dose distributions were compared with the corresponding measured dose distributions in the light of complexity of the plans and its deliverability. RESULTS The clinically significant impact of the plans complexity on their deliverability is visible only for the gamma passing rates analysis performed in a local mode and directly in the organs. While more general analyses show statistically significant differences, the clinical relevance of them has not been confirmed. The analysis showed that IG(ST)_OPT and IG(B)_OPT significantly differ from IG(ST) and IG(B). The clinical acceptance of IG(ST)_OPT obtained for hard combinations of gamma acceptance criteria (2%/2 mm) confirm its satisfactory deliverability. In turn, for IG(B)_OPT in the case of the rectum, the combination of 2%/2 mm did not meet the criteria of acceptance. CONCLUSION Despite the complexity of the IG(ST)_OPT, the results of analysis confirm the acceptance of its deliverability when 2%/2 mm gamma acceptance criteria are used during the analysis.
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Affiliation(s)
- Agata Jodda
- Department of Medical Physics, Greater Poland Cancer Centre, Garbary 15, 61-866 Poznań, Poland
| | - Tomasz Piotrowski
- Department of Medical Physics, Greater Poland Cancer Centre, Garbary 15, 61-866 Poznań, Poland
- Department of Electroradiology, Poznań University of Medical Sciences, Poznań, Poland
| | - Marta Kruszyna-Mochalska
- Department of Medical Physics, Greater Poland Cancer Centre, Garbary 15, 61-866 Poznań, Poland
- Department of Electroradiology, Poznań University of Medical Sciences, Poznań, Poland
| | - Julian Malicki
- Department of Medical Physics, Greater Poland Cancer Centre, Garbary 15, 61-866 Poznań, Poland
- Department of Electroradiology, Poznań University of Medical Sciences, Poznań, Poland
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Binny D, Spalding M, Crowe SB, Jolly D, Kairn T, Trapp JV, Walsh A. Investigating the use of aperture shape controller in VMAT treatment deliveries. Med Dosim 2020; 45:284-292. [PMID: 32223971 DOI: 10.1016/j.meddos.2020.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 01/14/2020] [Accepted: 02/13/2020] [Indexed: 01/21/2023]
Abstract
BACKGROUND Aperture shape controller (ASC) is a recently introduced leaf sequencer that controls the complexity of multileaf collimator apertures in the Photon Optimizer algorithm of the Eclipse treatment planning system. The aim of this study is to determine if the ASC can reduce plan complexity and improve verification results, without compromising plan quality. METHODS Thirteen plans grouped into cohorts of head and neck/brain, breast/chest and pelvis were reoptimised using the same optimization as the non-ASC setting for low, moderate and high ASC settings. These plans were analyzed using plan quality indices such as the conformity index and homogeneity index in addition to dose-volume histogram based analysis on PTVs and organ at risks. Complexity assessments were performed using metrics such as average leaf pair opening, modulation complexity scores, relative monitor units (MU) and treatment time. Monitor unit per gantry angle variations were also analyzed. A third-party algorithm was also used to assess 3D dose distributions produced using the new leaf sequencer tool. Deliverability for the final multileaf collimator distribution was quantified using portal dose image prediction based gamma analysis. RESULTS Plan conformality assessments showed comparable results and no significant plan degradation for plans reoptimised using ASC. Reduction in overall MU distributions were seen in some cases using higher ASC however, no overall trends were observed. In general, treatment deliverability, assessed using gamma analysis did not improve drastically however MU per degree distribution in 1 case improved when reoptimised using ASC. Treatment MUs generally reduced when ASC settings were used whilst in 1 case an increase in the treatment time factor > 1.8 was observed. The third-party algorithm assessment showed an underestimation of dose calculations for all cohorts used in this study when a higher ASC setting is used. CONCLUSIONS The impact of using ASC in treatment plans was characterised in this study. Although plan complexity marginally improved when using higher ASC settings, no consensus could be reached based on metrics analyzed in this study. A reduction in MU distribution was observed with increasing ASC settings in most cases. This study recommends that ASC to be used as an additional tool only to test its suitability to reduce plan complexity.
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Affiliation(s)
- Diana Binny
- ICON Cancer Centres, North Lakes, Australia; Queensland University of Technology, Brisbane, Australia.
| | | | - Scott B Crowe
- Queensland University of Technology, Brisbane, Australia; Cancer Care Services, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | | | - Tanya Kairn
- Queensland University of Technology, Brisbane, Australia; Cancer Care Services, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Jamie V Trapp
- Queensland University of Technology, Brisbane, Australia
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Kazantsev P, Lechner W, Gershkevitsh E, Clark CH, Venencia D, Van Dyk J, Wesolowska P, Hernandez V, Jornet N, Tomsej M, Bokulic T, Izewska J. IAEA methodology for on-site end-to-end IMRT/VMAT audits: an international pilot study. Acta Oncol 2020; 59:141-148. [PMID: 31746249 DOI: 10.1080/0284186x.2019.1685128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Background: The IAEA has developed and tested an on-site, end-to-end IMRT/VMAT dosimetry audit methodology for head and neck cases using an anthropomorphic phantom. The audit methodology is described, and the results of the international pilot testing are presented.Material and methods: The audit utilizes a specially designed, commercially available anthropomorphic phantom capable of accommodating a small volume ion chamber (IC) in four locations (three in planning target volumes (PTVs) and one in an organ at risk (OAR)) and a Gafchromic film in a coronal plane for the absorbed dose to water and two-dimensional dose distribution measurements, respectively. The audit consists of a pre-visit and on-site phases. The pre-visit phase is carried out remotely and includes a treatment planning task and a set of computational exercises. The on-site phase aims at comparing the treatment planning system (TPS) calculations with measurements in the anthropomorphic phantom following an end-to-end approach. Two main aspects were tested in the pilot study: feasibility of the planning constraints and the accuracy of IC and film results in comparison with TPS calculations. Treatment plan quality was scored from 0 to 100.Results: Forty-two treatment plans were submitted by 14 institutions from 10 countries, with 79% of them having a plan quality score over 90. Seventeen sets of IC measurement results were collected, and the average measured to calculated dose ratio was 0.988 ± 0.016 for PTVs and 1.020 ± 0.029 for OAR. For 13 film measurement results, the average gamma passing rate was 94.1% using criteria of 3%/3 mm, 20% threshold and global gamma.Conclusions: The audit methodology was proved to be feasible and ready to be adopted by national dosimetry audit networks for local implementation.
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Affiliation(s)
| | - Wolfgang Lechner
- Department of Radiation Oncology, Division of Medical Physics, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria
| | | | - Catharine H. Clark
- Department of Medical Physics, Royal Surrey County Hospital, Guildford, UK
- Metrology for Medical Physics (MEMPHYS), National Physical Laboratory, Teddington, UK
| | | | - Jacob Van Dyk
- Department of Oncology and Medical Biophysics, Western University, London, Canada
| | | | - Victor Hernandez
- Department of Medical Physics, Hospital Sant Joan de Reus, IISPV, Tarragona, Spain
| | - Nuria Jornet
- Servei de Radiofisica i Radioproteccio, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Milan Tomsej
- CHU Charleroi, Hopital Andre Vesale, Montigny-le-Tilleul, Belgium
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Santos T, Ventura T, Lopes MDC. Evaluation of the complexity of treatment plans from a national IMRT/VMAT audit – Towards a plan complexity score. Phys Med 2020; 70:75-84. [DOI: 10.1016/j.ejmp.2020.01.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 01/08/2020] [Accepted: 01/14/2020] [Indexed: 01/22/2023] Open
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Tambe NS, Pires IM, Moore C, Cawthorne C, Beavis AW. Validation of in-house knowledge-based planning model for advance-stage lung cancer patients treated using VMAT radiotherapy. Br J Radiol 2020; 93:20190535. [PMID: 31846347 DOI: 10.1259/bjr.20190535] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES Radiotherapy plan quality may vary considerably depending on planner's experience and time constraints. The variability in treatment plans can be assessed by calculating the difference between achieved and the optimal dose distribution. The achieved treatment plans may still be suboptimal if there is further scope to reduce organs-at-risk doses without compromising target coverage and deliverability. This study aims to develop a knowledge-based planning (KBP) model to reduce variability of volumetric modulated arc therapy (VMAT) lung plans by predicting minimum achievable lung volume-dose metrics. METHODS Dosimetric and geometric data collected from 40 retrospective plans were used to develop KBP models aiming to predict the minimum achievable lung dose metrics via calculating the ratio of the residual lung volume to the total lung volume. Model accuracy was verified by replanning 40 plans. Plan complexity metrics were calculated using locally developed script and their effect on treatment delivery was assessed via measurement. RESULTS The use of KBP resulted in significant reduction in plan variability in all three studied dosimetric parameters V5, V20 and mean lung dose by 4.9% (p = 0.007, 10.8 to 5.9%), 1.3% (p = 0.038, 4.0 to 2.7%) and 0.9 Gy (p = 0.012, 2.5 to 1.6Gy), respectively. It also increased lung sparing without compromising the overall plan quality. The accuracy of the model was proven as clinically acceptable. Plan complexity increased compared to original plans; however, the implication on delivery errors was clinically insignificant as demonstrated by plan verification measurements. CONCLUSION Our in-house model for VMAT lung plans led to a significant reduction in plan variability with concurrent decrease in lung dose. Our study also demonstrated that treatment delivery verifications are important prior to clinical implementation of KBP models. ADVANCES IN KNOWLEDGE In-house KBP models can predict minimum achievable lung dose-volume constraints for advance-stage lung cancer patients treated with VMAT. The study demonstrates that plan complexity could increase and should be assessed prior to clinical implementation.
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Affiliation(s)
- Nilesh S Tambe
- Radiation Physics, Queen's Centre for Oncology, University of Hull Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, UK.,Faculty of Health Sciences, University of Hull, Cottingham road, Hull, HU16 7RX, UK
| | - Isabel M Pires
- Faculty of Health Sciences, University of Hull, Cottingham road, Hull, HU16 7RX, UK
| | - Craig Moore
- Radiation Physics, Queen's Centre for Oncology, University of Hull Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, UK
| | - Christopher Cawthorne
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, Biomedical Sciences Group, KU LEUVEN, Herestraat 49, 3000, Leuven, Belgium
| | - Andrew W Beavis
- Radiation Physics, Queen's Centre for Oncology, University of Hull Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, UK.,Faculty of Health Sciences, University of Hull, Cottingham road, Hull, HU16 7RX, UK.,Faculty of Health and Well Being, Sheffield-Hallam University, Collegiate Crescent, Sheffield, S10 2BP, UK
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Wall PD, Fontenot JD. Application and comparison of machine learning models for predicting quality assurance outcomes in radiation therapy treatment planning. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100292] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
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2 Complexity indexes: A new approach to limit pre-treatment checks. Phys Med 2019. [DOI: 10.1016/j.ejmp.2019.09.083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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Li J, Zhang X, Li J, Jiang R, Sui J, Chan MF, Yang R. Impact of delivery characteristics on dose delivery accuracy of volumetric modulated arc therapy for different treatment sites. JOURNAL OF RADIATION RESEARCH 2019; 60:603-611. [PMID: 31147684 PMCID: PMC6805974 DOI: 10.1093/jrr/rrz033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Revised: 03/31/2019] [Indexed: 06/09/2023]
Abstract
This study aimed to investigate the impact of delivery characteristics on the dose delivery accuracy of volumetric modulated arc therapy (VMAT) for different treatment sites. The pretreatment quality assurance (QA) results of 344 VMAT patients diagnosed with gynecological (GYN), head and neck (H&N), rectal or prostate cancer were randomly chosen in this study. Ten metrics reflecting VMAT delivery characteristics were extracted from the QA plans. Compared with GYN and rectal plans, H&N and prostate plans had higher aperture complexity and monitor units (MU), and smaller aperture area. Prostate plans had the smallest aperture area and lowest leaf speed compared with other plans (P < 0.001). No differences in gantry speed were found among the four sites. The gamma passing rates (GPRs) of GYN, rectal and H&N plans were inversely associated with union aperture area (UAA) and leaf speed (Pearson's r: -0.39 to -0.68). GPRs of prostate plans were inversely correlated with aperture complexity, MU and small aperture score (SAS) (absolute Pearson's r: 0.34 to 0.49). Significant differences in GPR between high SAS and low SAS subgroups were found only when leaf speed was <0.42 cm s-1 (P < 0.001). No association of GPR with gantry speed was found in four sites. Leaf speed was more strongly associated with UAA. Aperture complexity and MU were more strongly associated with SAS. VMAT plans from different sites have distinct delivery characteristics. Affecting dose delivery accuracy, leaf speed is the key factor for GYN, rectal and H&N plans, while aperture complexity, MU and small apertures have a higher influence on prostate plans.
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Affiliation(s)
- Jiaqi Li
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Xile Zhang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Jun Li
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Rongtao Jiang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jing Sui
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Maria F Chan
- Medical Physics Department, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ruijie Yang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
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Chiavassa S, Bessieres I, Edouard M, Mathot M, Moignier A. Complexity metrics for IMRT and VMAT plans: a review of current literature and applications. Br J Radiol 2019; 92:20190270. [PMID: 31295002 PMCID: PMC6774599 DOI: 10.1259/bjr.20190270] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 07/04/2019] [Accepted: 07/09/2019] [Indexed: 12/21/2022] Open
Abstract
Modulated radiotherapy with multileaf collimators is widely used to improve target conformity and normal tissue sparing. This introduced an additional degree of complexity, studied by multiple teams through different properties. Three categories of complexity metrics were considered in this review: fluence, deliverability and accuracy metrics. The first part of this review is dedicated to the inventory of these complexity metrics. Different applications of these metrics emerged. Influencing the optimizer by integrating complexity metrics into the cost function has been little explored and requires more investigations. In modern treatment planning system, it remains confined to MUs or treatment time limitation. A large majority of studies calculated metrics only for analysis, without plan modification. The main application was to streamline the patient specific quality assurance workload, investigating the capability of complexity metrics to predict patient specific quality assurance results. Additionally complexity metrics were used to analyze behaviour of TPS optimizer, compare TPS, operators and plan properties, and perform multicentre audit. Their potential was also explored in the context of adaptive radiotherapy and automation planning. The second part of the review gives an overview of these studies based on the complexity metrics.
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Affiliation(s)
- Sophie Chiavassa
- Department of Medical Physics, Institut de Cancérologie de l’Ouest Centre René Gauducheau, 44805 Saint-Herblain, France
| | - Igor Bessieres
- Departement of Medical Physics, Centre Georges-François Leclerc, 1 rue Professeur Marion, 21000 Dijon, France
| | - Magali Edouard
- Department of Radiation Oncology, Gustave Roussy, 114 rue Édouard-Vaillant, 94805 Villejuif, France
| | - Michel Mathot
- Liege University Hospital, Domaine du Sart Tilman - B.35 - B-4000 LIEGE1, Belgium
| | - Alexandra Moignier
- Department of Medical Physics, Institut de Cancérologie de l’Ouest Centre René Gauducheau, 44805 Saint-Herblain, France
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Lam D, Zhang X, Li H, Deshan Y, Schott B, Zhao T, Zhang W, Mutic S, Sun B. Predicting gamma passing rates for portal dosimetry‐based IMRT QA using machine learning. Med Phys 2019; 46:4666-4675. [DOI: 10.1002/mp.13752] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 07/29/2019] [Accepted: 07/30/2019] [Indexed: 12/12/2022] Open
Affiliation(s)
- Dao Lam
- Department of Radiation Oncology Washington University School of Medicine 4921 Parkview Place, Campus Box 8224 St. Louis MO 63110USA
| | - Xizhe Zhang
- School of Computer Science and Engineering Northeastern University Shenyang Liaoning 110819China
| | - Harold Li
- Department of Radiation Oncology Washington University School of Medicine 4921 Parkview Place, Campus Box 8224 St. Louis MO 63110USA
| | - Yang Deshan
- Department of Radiation Oncology Washington University School of Medicine 4921 Parkview Place, Campus Box 8224 St. Louis MO 63110USA
| | - Brayden Schott
- Department of Radiation Oncology Washington University School of Medicine 4921 Parkview Place, Campus Box 8224 St. Louis MO 63110USA
| | - Tianyu Zhao
- Department of Radiation Oncology Washington University School of Medicine 4921 Parkview Place, Campus Box 8224 St. Louis MO 63110USA
| | - Weixiong Zhang
- Department of Computer Science and Engineering Washington University One Brookings Drive, CampusBox 1045 St. Louis MO 63130USA
| | - Sasa Mutic
- Department of Radiation Oncology Washington University School of Medicine 4921 Parkview Place, Campus Box 8224 St. Louis MO 63110USA
| | - Baozhou Sun
- Department of Radiation Oncology Washington University School of Medicine 4921 Parkview Place, Campus Box 8224 St. Louis MO 63110USA
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Antoine M, Ralite F, Soustiel C, Marsac T, Sargos P, Cugny A, Caron J. Use of metrics to quantify IMRT and VMAT treatment plan complexity: A systematic review and perspectives. Phys Med 2019; 64:98-108. [PMID: 31515041 DOI: 10.1016/j.ejmp.2019.05.024] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 05/24/2019] [Accepted: 05/26/2019] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Fixed-field intensity modulated radiation therapy (FF-IMRT) or volumetric modulated arc therapy (VMAT) beams complexity is due to fluence fluctuation. Pre-treatment Quality Assurance (PTQA) failure could be linked to it. Several plan complexity metrics (PCM) have been published to quantify this complexity but in a heterogeneous formalism. This review proposes to gather different PCM and to discuss their eventual PTQA failure identifier abilities. METHODS AND MATERIALS A systematic literature search and outcome extraction from MEDLINE/PubMed (National Center for Biotechnology Information, NCBI) was performed. First, a list and a synthesis of available PCM is made in a homogeneous formalism. Second, main results relying on the link between PCM and PTQA results but also on other uses are listed. RESULTS A total of 163 studies were identified and n = 19 were selected after inclusion and exclusion criteria application. Difference is made between fluence and degree of freedom (DOF)-based PCM. Results about the PCM potential as PTQA failure identifier are described and synthesized. Others uses are also found in quality, big data, machine learning and audit procedure. CONCLUSIONS A state of the art is made thanks to this homogeneous PCM classification. For now, PCM should be seen as a planning procedure quality indicator although PTQA failure identifier results are mitigated. However limited clinical use seems possible for some cases. Yet, addressing the general PTQA failure prediction case could be possible with the big data or machine learning help.
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Affiliation(s)
- Mikaël Antoine
- Service d'onco-radiothérapie, Polyclinique de Bordeaux Nord, 33000 Bordeaux, France; Department of Radiotherapy, Institut Bergonié, Comprehensive Cancer Centre, F-33000 Bordeaux, France.
| | - Flavien Ralite
- Department of Radiotherapy, Institut Bergonié, Comprehensive Cancer Centre, F-33000 Bordeaux, France; SUBATECH, IMT-Atlantique, CNRS/IN2P3, Université de Nantes, Nantes, France
| | - Charles Soustiel
- Department of Radiotherapy, Centre Hospitalier de Dax, Dax, France
| | - Thomas Marsac
- Department of Radiotherapy, Institut Bergonié, Comprehensive Cancer Centre, F-33000 Bordeaux, France
| | - Paul Sargos
- Department of Radiotherapy, Institut Bergonié, Comprehensive Cancer Centre, F-33000 Bordeaux, France
| | - Audrey Cugny
- Department of Radiotherapy, Institut Bergonié, Comprehensive Cancer Centre, F-33000 Bordeaux, France
| | - Jérôme Caron
- Department of Radiotherapy, Institut Bergonié, Comprehensive Cancer Centre, F-33000 Bordeaux, France
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Kairn T, Crowe SB. Retrospective analysis of breast radiotherapy treatment plans: Curating the 'non-curated'. J Med Imaging Radiat Oncol 2019; 63:517-529. [PMID: 31081603 DOI: 10.1111/1754-9485.12892] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 03/24/2019] [Indexed: 11/29/2022]
Abstract
INTRODUCTION This paper provides a demonstration of how non-curated data can be retrospectively cleaned, so that existing repositories of radiotherapy treatment planning data can be used to complete bulk retrospective analyses of dosimetric trends and other plan characteristics. METHODS A non curated archive of 1137 radiotherapy treatment plans accumulated over a 12-month period, from five radiotherapy centres operated by one institution, was used to investigate and demonstrate a process of clinical data cleansing, by: identifying and translating inconsistent structure names; correcting inconsistent lung contouring; excluding plans for treatments other than breast tangents and plans without identifiable PTV, lung and heart structures; and identifying but not excluding plans that deviated from the local planning protocol. PTV, heart and lung dose-volume metrics were evaluated, in addition to a sample of personnel and linac load indicators. RESULTS Data cleansing reduced the number of treatment plans in the sample by 35.7%. Inconsistent structure names were successfully identified and translated (e.g. 35 different names for lung). Automatically separating whole lung structures into left and right lung structures allowed the effect of contralateral and ipsilateral lung dose to be evaluated, while introducing some small uncertainties, compared to manual contouring. PTV doses were indicative of prescription doses. Breast treatment work was unevenly distributed between oncologists and between metropolitan and regional centres. CONCLUSION This paper exemplifies the data cleansing and data analysis steps that may be completed using existing treatment planning data, to provide individual radiation oncology departments with access to information on their own patient populations. Clearly, the well-planned and systematic recording of new, high quality data is the preferred solution, but the retrospective curation of non-curated data may be a useful interim solution, for radiation oncology departments where the systems for recording of new data have yet to be designed and agreed.
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Affiliation(s)
- Tanya Kairn
- Genesis Cancer Care, Auchenflower, Queensland, Australia.,Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Scott B Crowe
- Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia.,Cancer Care Services, Royal Brisbane and Women's Hospital, Herston, Queensland, Australia
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Granville DA, Sutherland JG, Belec JG, La Russa DJ. Predicting VMAT patient-specific QA results using a support vector classifier trained on treatment plan characteristics and linac QC metrics. Phys Med Biol 2019; 64:095017. [PMID: 30921785 DOI: 10.1088/1361-6560/ab142e] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The use of treatment plan characteristics to predict patient-specific quality assurance (QA) measurement results has recently been reported as a strategy to help facilitate automated pre-treatment verification workflows or to provide a virtual assessment of delivery quality. The goal of this work is to investigate the potential of using treatment plan characteristics and linac performance metrics (i.e. quality control test results) in combination with machine learning techniques to predict the results of VMAT patient-specific QA measurements. Using features that describe treatment plan complexity and linac performance metrics, we trained a linear support vector classifier (SVC) to classify the results of VMAT patient-specific QA measurements. The 'targets' in this model were simple classes representing median dose difference between measured and expected dose distributions-'hot' if the median dose deviation was >1%, 'cold' if it was <-1%, and 'normal' if it was within ±1%. A total of 1620 unique patient-specific QA measurements were available for model development and testing. 75% of the data were used to develop and cross-validate the model, and the remaining 25% were used for an independent assessment of model performance. For the model development phase, a recursive feature elimination (RFE) cross-validation technique was used to eliminate unimportant features. Model performance was assessed using receiver operator characteristic (ROC) curve metrics. Of the ten features found to be most predictive of patient-specific QA measurement results, half were derived from treatment plan characteristics and half from quality control (QC) metrics characterizing linac performance. The model achieved a micro-averaged area under the ROC curve of 0.93, and a macro-averaged area under the ROC curve of 0.88. This work demonstrates the potential of using both treatment plan characteristics and routine linac QC results in the development of machine learning models for VMAT patient-specific QA measurements.
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Affiliation(s)
- Dal A Granville
- Radiation Medicine Program, The Ottawa Hospital, Ottawa, Canada. Author to whom any correspondence should be addressed
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Glenn MC, Hernandez V, Saez J, Followill DS, Howell RM, Pollard-Larkin JM, Zhou S, Kry SF. Treatment plan complexity does not predict IROC Houston anthropomorphic head and neck phantom performance. Phys Med Biol 2018; 63:205015. [PMID: 30230475 PMCID: PMC6287268 DOI: 10.1088/1361-6560/aae29e] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Previous works indicate that intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) plans that are highly complex may produce more errors in dose calculation and treatment delivery. Multiple complexity metrics have been proposed and associated with IMRT QA results, but their relationships with plan performance using in situ dose measurements have not been thoroughly investigated. This study aimed to evaluate the relationships between IMRT treatment plan complexity and anthropomorphic phantom performance in order to assess the extent to which plan complexity is related to dosimetric performance in the IROC phantom credentialing program. Sixteen complexity metrics, including the modulation complexity score (MCS), several modulation indices, and total monitor units (MU) delivered, were evaluated for 343 head and neck phantom irradiations, comprising both IMRT (step-and-shoot and sliding window techniques) and VMAT. Spearman's correlations were used to explore the relationship between complexity and plan performance, as measured by the dosimetric differences between the treatment planning system (TPS) and thermoluminescent dosimeter (TLD) measurement, as well as film gamma analysis. Relationships were likewise determined for several combinations of subpopulations, based on the linear accelerator model, TPS used, and delivery modality. Evaluation of the complexity metrics presented here yielded no significant relationships (p > 0.01, Bonferroni-corrected) and all correlations were weak (less than ±0.30). These results indicate that complexity metrics have limited predictive utility in assessing plan performance in multi-institutional comparisons of IMRT plans. Other factors affecting plan accuracy, such as dosimetric modeling or multileaf collimator (MLC) performance, should be investigated to determine a more probable cause for dose delivery errors.
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Affiliation(s)
- Mallory C. Glenn
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Victor Hernandez
- Department of Medical Physics, Hospital Universitari Sant Joan de Reus, Tarragona, Spain
| | - Jordi Saez
- Department of Radiation Oncology, Hospital Clmic de Barcelona, Barcelona, Spain
| | - David S. Followill
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Rebecca M. Howell
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Julianne M. Pollard-Larkin
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Shouhao Zhou
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Stephen F. Kry
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
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Rijken J, Jordan B, Crowe S, Kairn T, Trapp J. Improving accuracy for stereotactic body radiotherapy treatments of spinal metastases. J Appl Clin Med Phys 2018; 19:453-462. [PMID: 29943895 PMCID: PMC6123175 DOI: 10.1002/acm2.12395] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Revised: 04/09/2018] [Accepted: 05/31/2018] [Indexed: 12/31/2022] Open
Abstract
PURPOSE Use of SBRT techniques is now a relatively common recourse for spinal metastases due to good local control rates and durable pain control. However, the technique has not yet reached maturity for gantry-based systems, so work is still required in finding planning approaches that produce optimum conformity as well as delivery for the slew of treatment planning systems and treatment machines. METHODS A set of 32 SBRT spine treatment plans based on four vertebral sites, varying in modality and number of control points, were created in Pinnacle. These plans were assessed according to complexity metrics and planning objectives as well as undergoing treatment delivery QA on an Elekta VersaHD through ion chamber measurement, ArcCheck, film-dose map comparison and MLC log-file reconstruction via PerFraction. RESULTS All methods of QA demonstrated statistically significant agreement with each other (r = 0.63, P < 0.001). Plan complexity and delivery accuracy were found to be independent of MUs (r = 0.22, P > 0.05) but improved with the number of control points (r = 0.46, P < 0.03); with use of 90 control points producing the most complex and least accurate plans. The fraction of small apertures used in treatment had no impact on plan quality or accuracy (r = 0.29, P > 0.05) but rather more complexly modulated plans showed poorer results due to MLC leaf position inaccuracies. Plans utilizing 180 and 240 control points produced optimal plan coverage with similar complexity metrics to each other. However, plans with 240 control points demonstrated slightly better delivery accuracy, with fewer MLC leaf position discrepancies. CONCLUSION In contrast to other studies, MU had no effect on delivery accuracy, with the most impactful parameter at the disposal of the planner being the number of control points utilized.
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Affiliation(s)
- James Rijken
- Genesis CareFlinders Private HospitalBedford ParkSAAustralia
- Queensland University of TechnologyBrisbaneQLDAustralia
| | - Barry Jordan
- Genesis CareFlinders Private HospitalBedford ParkSAAustralia
| | - Scott Crowe
- Queensland University of TechnologyBrisbaneQLDAustralia
- Royal Brisbane and Women's HospitalBrisbaneQLDAustralia
| | - Tanya Kairn
- Queensland University of TechnologyBrisbaneQLDAustralia
- Royal Brisbane and Women's HospitalBrisbaneQLDAustralia
| | - Jamie Trapp
- Royal Brisbane and Women's HospitalBrisbaneQLDAustralia
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Tamura M, Monzen H, Matsumoto K, Kubo K, Otsuka M, Inada M, Doi H, Ishikawa K, Nakamatsu K, Sumida I, Mizuno H, Yoon DK, Nishimura Y. Mechanical performance of a commercial knowledge-based VMAT planning for prostate cancer. Radiat Oncol 2018; 13:163. [PMID: 30170614 PMCID: PMC6119260 DOI: 10.1186/s13014-018-1114-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 08/23/2018] [Indexed: 12/03/2022] Open
Abstract
Background This study clarified the mechanical performance of volumetric modulated arc therapy (VMAT) plans for prostate cancer generated with a commercial knowledge-based treatment planning (KBP) and whether KBP system could be applied clinically without any major problems with mechanical performance. Methods Thirty consecutive prostate cancer patients who underwent VMAT using extant clinical plans were evaluated. The mechanical performance and dosimetric accuracy of the single optimized KBPs, which were trained with other 51 clinical plans, were compared with the clinical plans. The mechanical performance metrics were mean field area (MFA), mean asymmetry distance (MAD), cross-axis score (CAS), closed leaf score (CLS), small aperture score (SAS), leaf travel (LT), modulation complexity score (MCSv), and monitor unit (MU). The γ passing rates were evaluated with ArcCheck and EBT3 film. Results The mean mechanical performance metrics (clinical plan vs. KBP) were as follows: 18.28 cm2 vs. 17.25 cm2 (MFA), 21.08 mm vs. 20.47 mm (MAD), 0.54 vs. 0.55 (CAS), 0.040 vs. 0.051 (CLS), 0.20 vs. 0.23 (SAS5mm), 458.5 mm vs. 418.8 mm (LT), 0.27 vs. 0.27 (MCSv), and 618.2 vs. 622.1 (MU), respectively. Significant differences were observed for CLS and LT. The average γ passing rates (clinical plan vs. KBP) were as follows: 99.0% vs. 99.1% (3%/3 mm) and 92.4% vs. 92.5% (2%/2 mm) with ArcCHeck, and 99.5% vs. 99.4% (3%/3 mm) and 95.2% vs. 95.4% (2%/2 mm) with EBT3 film, respectively. Conclusions The KBP used lower multileaf collimator (MLC) travel and more closed or small MLC apertures than the clinical plan. The KBP system of VMAT for the prostate cancer was acceptable for clinical use without any major problems.
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Affiliation(s)
- Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Science, Kindai University, 377-2, Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Science, Kindai University, 377-2, Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan.
| | - Kenji Matsumoto
- Department of Medical Physics, Graduate School of Medical Science, Kindai University, 377-2, Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
| | - Kazuki Kubo
- Department of Medical Physics, Graduate School of Medical Science, Kindai University, 377-2, Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
| | - Masakazu Otsuka
- Department of Medical Physics, Graduate School of Medical Science, Kindai University, 377-2, Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
| | - Masahiro Inada
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, 377-2, Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
| | - Hiroshi Doi
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, 377-2, Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
| | - Kazuki Ishikawa
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, 377-2, Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
| | - Kiyoshi Nakamatsu
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, 377-2, Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
| | - Iori Sumida
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, 2-2, Yamada-oka, Suita, Osaka, 565-0071, Japan
| | - Hirokazu Mizuno
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, 2-2, Yamada-oka, Suita, Osaka, 565-0071, Japan
| | - Do-Kun Yoon
- Department of Biomedical Engineering and Research Institute of Biomedical Engineering, College of Medicine, Catholic University of Korea, 505, Banpo-dong, Seocho-gu, Seoul, 137-701, Korea
| | - Yasumasa Nishimura
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, 377-2, Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
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Hernandez V, Saez J, Pasler M, Jurado-Bruggeman D, Jornet N. Comparison of complexity metrics for multi-institutional evaluations of treatment plans in radiotherapy. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2018; 5:37-43. [PMID: 33458367 PMCID: PMC7807588 DOI: 10.1016/j.phro.2018.02.002] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 01/30/2018] [Accepted: 02/01/2018] [Indexed: 11/17/2022]
Abstract
Several complexity metrics were highly correlated and can be considered equivalent. Other metrics produced different results, especially for plans from different TPSs. Different TPSs prioritise modulation of different plan parameters. The ranking of plan complexity can greatly depend on the metric used. This must be carefully considered in multi-institutional plan comparisons.
Background and purpose It is known that intensity-modulated radiotherapy plans that are highly complex might be less accurate in dose calculation and treatment delivery. Multiple complexity metrics have been proposed, but the relationships between them have not been thoroughly investigated. This study investigated these relationships in multi-institutional comparisons of treatment plans, where plans from multiple treatment planning systems (TPSs) are typically evaluated. Materials and methods A program was developed to compute several complexity indices and provide analysis of dynamic plan parameters. This in-house software was used to analyse plans from a recent multi-institutional audit. Additionally, 100 clinical volumetric modulated arc therapy (VMAT) plans from two institutions using different TPSs were analysed. Results All plans produced satisfactory pre-treatment verification results and, hence, complexity metrics could not be used to predict plans failing QA. Regarding the relationship among complexity indices, some very strong correlations were found (r > 0.9 with p < 0.01). However, some relevant discrepancies between complexity indices were obtained, even with negative correlation coefficients (r ∼ −0.6) which were expected to be positive. These discrepancies could be explained because each complexity index focused on different features of the plan and different TPSs prioritised modulation of different plan parameters. Conclusions Some complexity indices provided similar information and can be considered equivalent. However, indices that focused on different plan parameters yielded different results and it was unclear which complexity index should be used. Careful consideration should be given to the use of complexity metrics in multi-institutional studies.
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Affiliation(s)
- Victor Hernandez
- Department of Medical Physics, Hospital Universitari Sant Joan de Reus, IISPV, Tarragona, Spain
| | - Jordi Saez
- Department of Radiation Oncology, Hospital Clínic de Barcelona, Spain
| | - Marlies Pasler
- Lake Constance Radiation Oncology Center Singen-Friedrichshafen, Germany
| | - Diego Jurado-Bruggeman
- Medical Physics and Radiation Protection Department, Institut Català d'Oncologia, Girona, Spain
| | - Nuria Jornet
- Servei de Radiofísica i Radioprotecció, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
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Binny D, Kairn T, Lancaster CM, Trapp JV, Crowe SB. Photon optimizer (PO) vs progressive resolution optimizer (PRO): a conformality- and complexity-based comparison for intensity-modulated arc therapy plans. Med Dosim 2017; 43:267-275. [PMID: 29079336 DOI: 10.1016/j.meddos.2017.10.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 10/04/2017] [Accepted: 10/10/2017] [Indexed: 12/24/2022]
Abstract
This study aimed to provide guidance on the advantages and limitations of a new optimizer, "photon optimizer" (PO), when compared with its predecessor, "progressive resolution optimizer" (PRO), for intensity-modulated arc therapy (IMAT) plans. Eleven study plans that included a cohort of prostate, head and neck, and brain treatment sites were optimized using both PRO and PO algorithms. A plan template using the same objectives for the same number of iterations was used for each optimized plan to obtain hypothetical treatment plans that would be comparable with a clinical plan. Analysis was performed using plan conformity-based parameters such as target volume coverage factor, conformation number and homogeneity indices, and plan complexity assessment parameters such as small aperture score, modulation indices, and monitor unit variation with arc angle for prostate, brain and head, and neck IMAT treatment plans. Plan conformality analysis demonstrated that conformation numbers, target volume coverage factors, and homogeneity indices produced by the 2 optimizers were comparable for most anatomic sites. IMAT treatment plans produced using the PRO optimizer were found to be less complex than plans produced using the PO optimizer, in terms of multileaf collimator (MLC) leaf position variability and modulation complexity scores. Similarly, the PRO optimizer was shown to produce treatment plans that used fewer monitor units (and generally fewer monitor unit per degree of arc rotation) than PO optimizer. This study demonstrated that the PO optimizer can produce IMAT treatment plans with a similar degree of dose conformity to the target volume and generally improved organ at risk sparing, compared with the PRO optimizer. Better coverage to organs at risk produced by plans optimized using PO was observed to have higher MLC variability and monitor units. Therefore, careful evaluation of treatment plan conformity and complexity before assessing its deliverability is recommended when implementing the routine use of PO optimizer.
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Affiliation(s)
- Diana Binny
- Radiation Oncology Centres, Redlands, Australia; Queensland University of Technology, Brisbane, Australia; Cancer Care Services, Royal Brisbane and Women's Hospital, Brisbane, Australia.
| | - Tanya Kairn
- Queensland University of Technology, Brisbane, Australia; Genesis Cancer Care Queensland, Brisbane, Australia
| | - Craig M Lancaster
- Cancer Care Services, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Jamie V Trapp
- Queensland University of Technology, Brisbane, Australia
| | - Scott B Crowe
- Queensland University of Technology, Brisbane, Australia; Cancer Care Services, Royal Brisbane and Women's Hospital, Brisbane, Australia
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