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Oh DOH, Choi JH, Ryu H, Chun M. Quantification of Treatment Plan Deliverability in Breast Volumetric-modulated Arc Therapy With Agility Multi-leaf Collimator. In Vivo 2024; 38:2254-2260. [PMID: 39187370 DOI: 10.21873/invivo.13690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/21/2024] [Accepted: 06/24/2024] [Indexed: 08/28/2024]
Abstract
BACKGROUND/AIM The aim was to assess the complexity of breast volumetric-modulated arc therapy (VMAT) plans using various indices and to evaluate their performance through gamma analysis in predicting plan deliverability. MATERIALS AND METHODS A total of 285 VMAT plans for 260 patients were created using the VersaHD™ linear accelerator with a Monaco treatment planning system. Corresponding verification plans were generated using the ArcCHECK® detector, and gamma analysis was conducted employing various criteria. Twenty-eight plan complexity metrics were computed, and Pearson's correlation coefficients were determined between the gamma passing rate (GPR) and these metrics. RESULTS The average GPR values for all plans were 97.7%, 89.9%, and 78.0% for the 2 mm/2%, 1 mm/2%, and 1 mm/1% criteria, respectively. While most complexity metrics exhibited weak correlations with GPRs under the 2 mm/2% criterion, leaf sequence variability (LSV), plan-averaged beam area (PA), converted area metric (CAM), and edge area metric (EAM) demonstrated the most robust performance, with Pearson's correlation coefficients of 0.57, 0.50, -0.70, and -0.56, respectively. CONCLUSION Metrics related to beam aperture size and irregularity, such as LSV, PA, CAM and EAM, proved to be reasonable predictors of plan deliverability in breast VMAT.
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Affiliation(s)
- DO Hoon Oh
- Department of Radiation Oncology, Chung-Ang University Gwang Myeong Hospital, Gyeonggi-do, Republic of Korea
| | - Jin Hwa Choi
- Department of Radiation Oncology, Chung-Ang University College of Medicine, Seoul, Republic of Korea
| | - Hyejo Ryu
- Department of Radiation Oncology, Chung-Ang University Gwang Myeong Hospital, Gyeonggi-do, Republic of Korea
| | - Minsoo Chun
- Department of Radiation Oncology, Chung-Ang University Gwang Myeong Hospital, Gyeonggi-do, Republic of Korea;
- Clinical Research Center, Chung-Ang University Gwang Myeong Hospital, Gyeonggi-do, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
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Xue X, Luan S, Ding Y, Li X, Li D, Wang J, Ma C, Jiang M, Wei W, Wang X. Treatment plan complexity quantification for predicting gamma passing rates in patient-specific quality assurance for stereotactic volumetric modulated arc therapy. J Appl Clin Med Phys 2024:e14432. [PMID: 38889335 DOI: 10.1002/acm2.14432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 05/11/2024] [Accepted: 05/21/2024] [Indexed: 06/20/2024] Open
Abstract
PURPOSE To investigate the beam complexity of stereotactic Volumetric Modulated Arc Therapy (VMAT) plans quantitively and predict gamma passing rates (GPRs) using machine learning. METHODS The entire dataset is exclusively made of stereotactic VMAT plans (301 plans with 594 beams) from Varian Edge LINAC. The GPRs were analyzed using Varian's portal dosimetry with 2%/2 mm criteria. A total of 27 metrics were calculated to investigate the correlation between metrics and GPRs. Random forest and gradient boosting models were developed and trained to predict the GPRs based on the extracted complexity features. The threshold values of complexity metric were obtained to predict a given beam to pass or fail from ROC curve analysis. RESULTS The three moderately significant values of Spearman's rank correlation to GPRs were 0.508 (p < 0.001), 0.445 (p < 0.001), and -0.416 (p < 0.001) for proposed metric LAAM, the ratio of the average aperture area over jaw area (AAJA) and index of modulation, respectively. The random forest method achieved 98.74% prediction accuracy with mean absolute error of 1.23% using five-fold cross-validation, and 98.71% with 1.25% for gradient boosting regressor method, respectively. LAAM, leaf travelling distance (LT), AAJA, LT modulation complexity score (LTMCS) and index of modulation, were the top five most important complexity features. The LAAM metric showed the best performance with AUC value of 0.801, and threshold value of 0.365. CONCLUSIONS The calculated metrics were effective in quantifying the complexity of stereotactic VMAT plans. We have demonstrated that the GPRs could be accurately predicted using machine learning methods based on extracted complexity metrics. The quantification of complexity and machine learning methods have the potential to improve stereotactic treatment planning and identify the failure of QA results promptly.
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Affiliation(s)
- Xudong Xue
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shunyao Luan
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Optoelectronic Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Ding
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangbin Li
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dan Li
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jingya Wang
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chi Ma
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Man Jiang
- Department of Nuclear Engineering and Technology, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Wei
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
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Zou Z, Gong C, Zeng L, Guan Y, Huang B, Yu X, Liu Q, Zhang M. Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:60-71. [PMID: 38343215 DOI: 10.1007/s10278-023-00930-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/13/2023] [Accepted: 10/13/2023] [Indexed: 03/02/2024]
Abstract
Pretreatment patient-specific quality assurance (prePSQA) is conducted to confirm the accuracy of the radiotherapy dose delivered. However, the process of prePSQA measurement is time consuming and exacerbates the workload for medical physicists. The purpose of this work is to propose a novel deep learning (DL) network to improve the accuracy and efficiency of prePSQA. A modified invertible and variable augmented network was developed to predict the three-dimensional (3D) measurement-guided dose (MDose) distribution of 300 cancer patients who underwent volumetric modulated arc therapy (VMAT) between 2018 and 2021, in which 240 cases were randomly selected for training, and 60 for testing. For simplicity, the present approach was termed as "IVPSQA." The input data include CT images, radiotherapy dose exported from the treatment planning system, and MDose distribution extracted from the verification system. Adam algorithm was used for first-order gradient-based optimization of stochastic objective functions. The IVPSQA model obtained high-quality 3D prePSQA dose distribution maps in head and neck, chest, and abdomen cases, and outperformed the existing U-Net-based prediction approaches in terms of dose difference maps and horizontal profiles comparison. Moreover, quantitative evaluation metrics including SSIM, MSE, and MAE demonstrated that the proposed approach achieved a good agreement with ground truth and yield promising gains over other advanced methods. This study presented the first work on predicting 3D prePSQA dose distribution by using the IVPSQA model. The proposed method could be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.
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Affiliation(s)
- Zhongsheng Zou
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Changfei Gong
- Department of Radiation Oncology, 1st Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lingpeng Zeng
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Yu Guan
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Bin Huang
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Xiuwen Yu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China.
| | - Minghui Zhang
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China.
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Koganezawa AS, Matsuura T, Kawahara D, Nakashima T, Shiba E, Murakami Y, Nagata Y. Unbiased evaluation of predicted gamma passing rate by an event-mixing technique. Med Phys 2024; 51:5-17. [PMID: 38009570 DOI: 10.1002/mp.16848] [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: 06/21/2023] [Revised: 10/31/2023] [Accepted: 10/31/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Predicting models of the gamma passing rate (GPR) have been studied to substitute the measurement-based gamma analysis. Since these studies used data from different radiotherapy systems comprising TPS, linear accelerator, and detector array, it has been difficult to compare the performances of the predicting models among institutions with different radiotherapy systems. PURPOSE We aimed to develop unbiased scoring methods to evaluate the performance of the models predicting the GPR, by introducing both best and worst limits for the performance of the GPR prediction. METHODS Two hundred head-and-neck VMAT plans were used to develop a framework. The GPRs were measured using the ArcCHECK device. The predicted GPR [p] was generated using a deep learning-based model [pDL ]. The predicting model was evaluated using four metrics: standard deviation (SD) [σ], Pearson's correlation coefficient (CC) [r], mean squared error (MSE) [s], and mean absolute error (MAE) [a]. The best limit [σ m ${\sigma _m}$ ,r m ${r_m}$ ,s m ${s_m}$ , anda m ${a_m}$ ] was estimated by measuring the SD of measured GPR [m] by shifting the device along the longitudinal direction to measure different sampling points. Mimicked best and worst p's [pbest and pworst ] were generated from pDL . The worst limit was defined such that m and p have no correlation [CC ∼ 0]. The worst limit [σMix , rMix , sMix , and aMix ] was generated using the event-mixing (EM) technique originally introduced in high-energy physics experiments. The range of σ, r, s, and a was defined to be[ σ m , σ Mix ] $[ {{\sigma _m},{\sigma _{{\mathrm{Mix}}}}} ]$ ,[ 0 , r m ] $[ {0,{r_m}} ]$ ,[ s m , s Mix ] $[ {{s_m},{s_{{\mathrm{Mix}}}}} ]$ , and[ a m , a Mix ] $[ {{a_m},{a_{{\mathrm{Mix}}}}} ]$ . The achievement score (AS) independently based on σ, r, s, and a were calculated for pDL , pbest and pworst . The probability that p fails the gamma analysis (alert frequency; AF) was estimated as a function ofσ d ${\sigma _d}$ values within the [σ m ${\sigma _m}$ , σMix ] range for the 3%/2 mm data with a 95% criterion. RESULTS SDs of the best limit were well reproduced byσ m = 0.531 100 - m ${\sigma _m} = \;0.531\sqrt {100 - m} $ . The EM technique successfully generated the( m , p ) $( {m,p} )$ pairs with no correlation. The AS using four metrics showed good agreement. This agreement indicates successful definitions of both best and worst limits, consistent definitions of the AS, and successful generations of mixed events. The AF for the DL-based model with the 3%/2 mm tolerance was 31.5% and 63.0% with CL's 99% and 99.9%, respectively. CONCLUSION We developed the AS to evaluate the predicting model of the GPR in an unbiased manner by excluding the effects of the precision of the radiotherapy system and the spreading of the GPR. The best and worst limits of the GPR prediction were successfully generated using the measured precision of the GPR and the EM technique, respectively. The AS andσ p ${\sigma _p}$ are expected to enable objective evaluation of the predicting model and setting exact achievement goal of precision for the predicted GPR.
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Affiliation(s)
- Akito S Koganezawa
- Department of Radiation Oncology, Hiroshima University Hospital, Hiroshima, Japan
- Department of Information and Electronic Engineering, Faculty of Science and Engineering, Teikyo University, Tochigi, Japan
| | - Takaaki Matsuura
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Hiroshima University Hospital, Hiroshima, Japan
| | - Takeo Nakashima
- Department of Clinical Support, Radiation Therapy Section, Hiroshima University Hospital, Hiroshima, Japan
| | - Eiji Shiba
- Department of Radiation Oncology, Hospital of the University of Occupational and Environmental Health, Fukuoka, Japan
| | - Yuji Murakami
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yasushi Nagata
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
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Ding Z, Kang K, Yuan Q, Zhang W, Sang Y. A Beam Angle Selection Method to Improve Plan Robustness Against Position Error in Intensity-Modulated Radiotherapy for Left-Sided Breast Cancer. Technol Cancer Res Treat 2024; 23:15330338241259633. [PMID: 38887092 PMCID: PMC11185013 DOI: 10.1177/15330338241259633] [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] [Indexed: 06/20/2024] Open
Abstract
PURPOSE We report a dosimetric study in whole breast irradiation (WBI) of plan robustness evaluation against position error with two radiation techniques: tangential intensity-modulated radiotherapy (T-IMRT) and multi-angle IMRT (M-IMRT). METHODS Ten left-sided patients underwent WBI were selected. The dosimetric characteristics, biological evaluation and plan robustness were evaluated. The plan robustness quantification was performed by calculating the dose differences (Δ) of the original plan and perturbed plans, which were recalculated by introducing a 3-, 5-, and 10-mm shift in 18 directions. RESULTS M-IMRT showed better sparing of high-dose volume of organs at risk (OARs), but performed a larger low-dose irradiation volume of normal tissue. The greater shift worsened plan robustness. For a 10-mm perturbation, greater dose differences were observed in T-IMRT plans in nearly all directions, with higher ΔD98%, ΔD95%, and ΔDmean of CTV Boost and CTV. A 10-mm shift in inferior (I) direction induced CTV Boost in T-IMRT plans a 1.1 (ΔD98%), 1.1 (ΔD95%), and 1.7 (ΔDmean) times dose differences greater than dose differences in M-IMRT plans. For CTV Boost, shifts in the right (R) and I directions generated greater dose differences in T-IMRT plans, while shifts in left (L) and superior (S) directions generated larger dose differences in M-IMRT plans. For CTV, T-IMRT plans showed higher sensitivity to a shift in the R direction. M-IMRT plans showed higher sensitivity to shifts in L, S, and I directions. For OARs, negligible dose differences were found in V20 of the lungs and heart. Greater ΔDmax of the left anterior descending artery (LAD) was seen in M-IMRT plans. CONCLUSION We proposed a plan robustness evaluation method to determine the beam angle against position uncertainty accompanied by optimal dose distribution and OAR sparing.
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Affiliation(s)
- Zhen Ding
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Kailian Kang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Qingqing Yuan
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Wenjue Zhang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Yong Sang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
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Ishizaka N, Kinoshita T, Sakai M, Tanabe S, Nakano H, Tanabe S, Nakamura S, Mayumi K, Akamatsu S, Nishikata T, Takizawa T, Yamada T, Sakai H, Kaidu M, Sasamoto R, Ishikawa H, Utsunomiya S. Prediction of patient-specific quality assurance for volumetric modulated arc therapy using radiomics-based machine learning with dose distribution. J Appl Clin Med Phys 2024; 25:e14215. [PMID: 37987544 PMCID: PMC10795425 DOI: 10.1002/acm2.14215] [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: 03/09/2023] [Revised: 09/29/2023] [Accepted: 10/30/2023] [Indexed: 11/22/2023] Open
Abstract
PURPOSE We sought to develop machine learning models to predict the results of patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), which were represented by several dose-evaluation metrics-including the gamma passing rates (GPRs)-and criteria based on the radiomic features of 3D dose distribution in a phantom. METHODS A total of 4,250 radiomic features of 3D dose distribution in a cylindrical dummy phantom for 140 arcs from 106 clinical VMAT plans were extracted. We obtained the following dose-evaluation metrics: GPRs with global and local normalization, the dose difference (DD) in 1% and 2% passing rates (DD1% and DD2%) for 10% and 50% dose threshold, and the distance-to-agreement in 1-mm and 2-mm passing rates (DTA1 mm and DTA2 mm) for 0.5%/mm and 1.0%.mm dose gradient threshold determined by measurement using a diode array in patient-specific QA. The machine learning regression models for predicting the values of the dose-evaluation metrics using the radiomic features were developed based on the elastic net (EN) and extra trees (ET) models. The feature selection and tuning of hyperparameters were performed with nested cross-validation in which four-fold cross-validation is used within the inner loop, and the performance of each model was evaluated in terms of the root mean square error (RMSE), the mean absolute error (MAE), and Spearman's rank correlation coefficient. RESULTS The RMSE and MAE for the developed machine learning models ranged from <1% to nearly <10% depending on the dose-evaluation metric, the criteria, and dose and dose gradient thresholds used for both machine learning models. It was advantageous to focus on high dose region for predicating global GPR, DDs, and DTAs. For certain metrics and criteria, it was possible to create models applicable for patients' heterogeneity by training only with dose distributions in phantom. CONCLUSIONS The developed machine learning models showed high performance for predicting dose-evaluation metrics especially for high dose region depending on the metric and criteria. Our results demonstrate that the radiomic features of dose distribution can be considered good indicators of the plan complexity and useful in predicting measured dose evaluation metrics.
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Affiliation(s)
- Natsuki Ishizaka
- Department of RadiologyNiigata Prefectural Shibata HospitalShibata CityNiigataJapan
| | - Tomotaka Kinoshita
- Department of Radiological TechnologyNiigata University Graduate School of Health SciencesNiigata CityNiigataJapan
| | - Madoka Sakai
- Department of RadiologyNagaoka Chuo General HospitalNagaokaNiigataJapan
- Department of Radiation OncologyNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
| | - Shunpei Tanabe
- Department of Radiation OncologyNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
| | - Hisashi Nakano
- Department of Radiation OncologyNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
| | - Satoshi Tanabe
- Department of Radiation OncologyNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
| | - Sae Nakamura
- Department of Radiation OncologyNiigata Neurosurgical HospitalNiigata CityNiigataJapan
| | - Kazuki Mayumi
- Department of Radiological TechnologyNiigata University Graduate School of Health SciencesNiigata CityNiigataJapan
| | - Shinya Akamatsu
- Department of Radiological TechnologyNiigata University Graduate School of Health SciencesNiigata CityNiigataJapan
- Department of RadiologyTakeda General HospitalAizuwakamatsu CityFukushimaJapan
| | - Takayuki Nishikata
- Department of Radiological TechnologyNiigata University Graduate School of Health SciencesNiigata CityNiigataJapan
- Division of RadiologyNagaoka Red Cross HospitalNagaoka‐shiNiigataJapan
| | - Takeshi Takizawa
- Department of Radiation OncologyNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
- Department of Radiation OncologyNiigata Neurosurgical HospitalNiigata CityNiigataJapan
| | - Takumi Yamada
- Section of Radiology, Department of Clinical SupportNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
| | - Hironori Sakai
- Section of Radiology, Department of Clinical SupportNiigata University Medical and Dental HospitalNiigata CityNiigataJapan
| | - Motoki Kaidu
- Department of Radiology and Radiation OncologyNiigata University Graduate School of Medical and Dental SciencesNiigata CityNiigataJapan
| | - Ryuta Sasamoto
- Department of Radiological TechnologyNiigata University Graduate School of Health SciencesNiigata CityNiigataJapan
| | - Hiroyuki Ishikawa
- Department of Radiology and Radiation OncologyNiigata University Graduate School of Medical and Dental SciencesNiigata CityNiigataJapan
| | - Satoru Utsunomiya
- Department of Radiological TechnologyNiigata University Graduate School of Health SciencesNiigata CityNiigataJapan
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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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8
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Kowatsch M, Szeverinski P, Clemens P, Künzler T, Söhn M, Alber M. Sensitivity and specificity of Monte Carlo based independent secondary dose computation for detecting modulation-related dose errors in intensity modulated radiotherapy. Z Med Phys 2023:S0939-3889(23)00117-4. [PMID: 37891103 DOI: 10.1016/j.zemedi.2023.10.001] [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: 01/25/2023] [Revised: 08/09/2023] [Accepted: 10/06/2023] [Indexed: 10/29/2023]
Abstract
BACKGROUND The recent availability of Monte Carlo based independent secondary dose calculation (ISDC) for patient-specific quality assurance (QA) of modulated radiotherapy requires the definition of appropriate, more sensitive action levels, since contemporary recommendations were defined for less accurate ISDC dose algorithms. PURPOSE The objective is to establish an optimum action level and measure the efficacy of a Monte Carlo ISDC software for pre-treatment QA of intensity modulated radiotherapy treatments. METHODS The treatment planning system and the ISDC were commissioned by their vendors from independent base data sets, replicating a typical real-world scenario. In order to apply Receiver-Operator-Characteristics (ROC), a set of treatment plans for various case classes was created that consisted of 190 clinical treatment plans and 190 manipulated treatment plans with dose errors in the range of 1.5-2.5%. All 380 treatment plans were evaluated with ISDC in the patient geometry. ROC analysis was performed for a number of Gamma (dose-difference/distance-to-agreement) criteria. QA methods were ranked according to Area under the ROC curve (AUC) and optimum action levels were derived via Youden's J statistics. RESULTS Overall, for original treatment plans, the mean Gamma pass rate (GPR) for Gamma(1%, 1 mm) was close to 90%, although with some variation across case classes. The best QA criterion was Gamma(2%, 1 mm) with GPR > 90% and an AUC of 0.928. Gamma criteria with small distance-to-agreement had consistently higher AUC. GPR of original treatment plans depended on their modulation degree. An action level in terms of Gamma(1%, 1 mm) GPR that decreases with modulation degree was the most efficient criterion with sensitivity = 0.91 and specificity = 0.95, compared with Gamma(3%, 3 mm) GPR > 99%, sensitivity = 0.73 and specificity = 0.91 as a commonly used action level. CONCLUSIONS ISDC with Monte Carlo proves highly efficient to catch errors in the treatment planning process. For a Monte Carlo based TPS, dose-difference criteria of 2% or less, and distance-to-agreement criteria of 1 mm, achieve the largest AUC in ROC analysis.
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Affiliation(s)
- Matthias Kowatsch
- Institute of Medical Physics, Academic Teaching Hospital Feldkirch, Carinagasse 47, 6800 Feldkirch, Austria.
| | - Philipp Szeverinski
- Institute of Medical Physics, Academic Teaching Hospital Feldkirch, Carinagasse 47, 6800 Feldkirch, Austria
| | - Patrick Clemens
- Department of Radio-Oncology, Academic Teaching Hospital Feldkirch, Carinagasse 47, 6800 Feldkirch, Austria
| | - Thomas Künzler
- Institute of Medical Physics, Academic Teaching Hospital Feldkirch, Carinagasse 47, 6800 Feldkirch, Austria
| | - Matthias Söhn
- Scientific-RT GmbH, Welserstr. 7, 81373 München, Germany
| | - Markus Alber
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Scientific-RT GmbH, Welserstr. 7, 81373 München, Germany
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Rolland J, Favrel V, Fau P, Mailleux H, Tallet A. Dosimetric comparison of VMAT standard optimization (SO) and multi-criteria optimization (MCO) treatment plans with standard mode delivery (STD) or sliding window (SW) for head and neck cancer. J Appl Clin Med Phys 2023; 24:e14013. [PMID: 37144958 PMCID: PMC10476993 DOI: 10.1002/acm2.14013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 03/28/2023] [Accepted: 04/18/2023] [Indexed: 05/06/2023] Open
Abstract
PURPOSE A new development on the RayStation treatment planning system (TPS) allows a plan to be planned by imposing a constraint on the leaf sequencing: all leaves move in the same direction before moving again in the opposite direction to create a succession of sliding windows (SWs). The study aims to investigate this new leaf sequencing, coupled with standard optimization (SO) and multi-criteria optimization (MCO) and to compare it with the standard sequencing (STD). METHODS Sixty plans were replanned for 10 head and neck cancer patients (two dose levels simultaneously SIB, 56 and 70 Gy in 35 fractions). All plans were compared, and a Wilcoxon signed-rank test was performed. Pre-processing QA and metrics of multileaf collimator (MLC) complexity were studied. RESULTS All methodologies met the dose requirements for the planning target volumes (PTVs) and organs at risk (OARs). SO demonstrates significantly best results for homogeneity index (HI), conformity index (CI), and target coverage (TC). SO-SW gives best results for PTVs (D98% and D2% ) but the differences between techniques are less than 1%. Only the D2%,PTV-56 Gy is higher with both MCO methods. MCO-STD offer the best sparing OARs (parotids, spinal cord, larynx, oral cavity). The gamma passing rates (GPRs) with 3%/3 mm criteria between the measured and calculated dose distributions are higher than 95%, slightly lowest with SW. The number of monitor units (MUs) and MLC metrics are higher in SW show a higher modulation. CONCLUSIONS All plans are feasible for the treatment. A clear advantage of SO-SW is that the treatment plan is more straightforward to planning by the user due to the more advanced modulation. MCO stands out for its ease of use and will allow a less experienced user to offer a better plan than in SO. In addition, MCO-STD will reduce the dose to the OARs while maintaining good TC.
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Affiliation(s)
- Julien Rolland
- Department of Medical PhysicsCentre Hospitalier InterCommunal des Alpes du SudGapFrance
- Department of Medical PhysicsInstitut Paoli CalmettesMarseilleFrance
| | - Véronique Favrel
- Department of RadiotherapyInstitut Paoli CalmettesMarseilleFrance
| | - Pierre Fau
- Department of Medical PhysicsInstitut Paoli CalmettesMarseilleFrance
| | - Hugues Mailleux
- Department of Medical PhysicsInstitut Paoli CalmettesMarseilleFrance
| | - Agnès Tallet
- Department of RadiotherapyInstitut Paoli CalmettesMarseilleFrance
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10
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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; 96:20220302. [PMID: 37129359 PMCID: PMC10321263 DOI: 10.1259/bjr.20220302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 03/12/2023] [Accepted: 04/02/2023] [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)
| | - 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
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11
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An effective and optimized patient-specific QA workload reduction for VMAT plans after MLC-modelling optimization. Phys Med 2023; 107:102548. [PMID: 36842260 DOI: 10.1016/j.ejmp.2023.102548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/16/2023] [Accepted: 02/13/2023] [Indexed: 02/26/2023] Open
Abstract
INTRODUCTION Many complexity metrics characterize modulated plans. First, this study aimed at identify the optimal complexity metrics to reduce workload associated to patient-specific quality assurance (PSQA) for our equipment and processes. Second, it intended to optimize our MLC modelling to improve measurement and calculation agreement with expectation of further reducing PSQA workload. METHODS Correlation and sensitivity at specificity equals to 1 were evaluated for PSQA results and different complexity metrics. Thresholds to stop PSQA were determined. After validation of the optimal complexity metric and threshold for our equipment and process, the MLC modelling was reviewed with a recently published methodology. This method is based on measurements with a Farmer-type ionization chamber of synchronous and asynchronous sweeping gap plans. Effect on the PSQA results and the identified threshold was investigated. RESULTS In our center, the most appropriate complexity metric for reducing our PSQA workload was the Modulation Complexity Score for VMAT (MCSv). The optimization of the MLC modelling significantly reduced the number of controlled plans, specifically for one of our two Varian Clinac. Any plan with a MCSv >= 0.34 is treated without PSQA. CONCLUSION This study rationalized and reduced our PSQA workload by approximately 30%. It is a continuing work with new TPS, machine or PSQA equipment. It encourages centers to re-evaluate their MLC modelling as well as assess the benefit of complexity metrics to streamline their PSQA workflow. An easier access, at least for reporting, at best for optimizing plans, into the TPS would be beneficial for the community.
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12
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Matsuura T, Kawahara D, Saito A, Yamada K, Ozawa S, Nagata Y. A synthesized gamma distribution-based patient-specific VMAT QA using a generative adversarial network. Med Phys 2023; 50:2488-2498. [PMID: 36609669 DOI: 10.1002/mp.16210] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 12/23/2022] [Accepted: 12/25/2022] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI)-based gamma passing rate (GPR) prediction has been proposed as a time-efficient virtual patient-specific QA method for the delivery of volumetric modulation arc therapy (VMAT). However, there is a limitation that the GPR value loses the locational information of dose accuracy. PURPOSE The objective was to predict the failing points in the gamma distribution and the GPR using a synthesized gamma distribution of VMAT QA with a deep convolutional generative adversarial network (GAN). METHODS The fluence maps of 270 VMAT beams for prostate cancer were measured using an electronic portal imaging device and analyzed using gamma evaluation with 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances. The 270 gamma distributions were divided into two datasets: 240 training datasets for creating a model and 30 test datasets for evaluation. The image prediction network for the fluence maps calculated by the treatment planning system (TPS) to the gamma distributions was created using a GAN. The sensitivity, specificity, and accuracy of detecting failing points were evaluated using measured and synthesized gamma distributions. In addition, the difference between measured GPR (mGPR) and predicted GPR (pGPR) values calculated from the synthesized gamma distributions was evaluated. RESULTS The root mean squared errors between mGPR and pGPR were 1.0%, 2.1%, 3.5%, and 3.6% for the 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances, respectively. The accuracies for detecting failing points were 98.9%, 96.9%, 94.7%, and 93.7% for 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances, respectively. The sensitivity and specificity were the highest for 1%/0.5-mm and 3%/2-mm tolerances, which were 82.7% and 99.6%, respectively. CONCLUSIONS We developed a novel system using a GAN to generate a synthesized gamma distribution-based patient-specific VMAT QA. The system is promising from the point of view of quality assurance in radiotherapy because it shows high performance and can detect failing points.
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Affiliation(s)
- Takaaki Matsuura
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan.,Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Akito Saito
- Department of Radiation Oncology, Hiroshima University Hospital, Hiroshima, Japan
| | - Kiyoshi Yamada
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
| | - Shuichi Ozawa
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan.,Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yasushi Nagata
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan.,Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
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Viola P, Romano C, Craus M, Macchia G, Buwenge M, Indovina L, Valentini V, Morganti AG, Deodato F, Cilla S. Prediction of VMAT delivery accuracy using plan modulation complexity score and log-files analysis. Biomed Phys Eng Express 2022; 8. [PMID: 35858537 DOI: 10.1088/2057-1976/ac82c6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 07/20/2022] [Indexed: 11/12/2022]
Abstract
The purpose of this study was to develop a predictive model based on plan complexity metrics and linac log-files analysis to classify the dosimetric accuracy of VMAT plans. A total of 612 VMAT plans, corresponding to 1224 arcs, were analyzed. All VMAT arcs underwent pre-treatment verification that was performed by means of the dynamic log-files generated by the linac. The comparison of predicted (by TPS) and measured (by log-files) integral fluences was performed using γ-analysis in terms of the percentage of points with γ-value smaller than one (γ%) and using a stringent 2%(local)/2mm criteria. This γ-analysis was performed by a commercial software LinacWatch. The action limits (AL) were derived from the mean values, standard deviations and the confidence limit (CL) of the γ% distribution. A plan complexity metric, the modulation complexity score (MCS), based on the aperture beam area variability and leaf sequence variability was used as input variable of the model. A binary logistic regression (LR) model was developed to classify QA results as "pass" (γ%≥AL) or "fail" (γ%<AL). Receiver operator characteristics (ROC) curves were used to determine the optimal MCS threshold to flag "failed" plans that need to be re-optimized. The model reliability was evaluated stratifying the plans in training, validation and testing groups. The confidence and action limits for γ% were found 20.1% and 79.9%, respectively. The accuracy of the model for the training and testing dataset was 97.4% and 98.0%, respectively. The optimal MCS threshold value for the identification of failed plans was 0.142, providing a true positive rate able to flag the plans failing QA of 91%. In clinical routine, the use of this MCS threshold may allow the prompt identification of overly modulated plans, then reducing the number of QA failures and improving the quality of VMAT plans used for treatment.
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Affiliation(s)
- Pietro Viola
- Gemelli Molise Hospital, Largo Gemelli 1, Campobasso, 86100, ITALY
| | - Carmela Romano
- Gemelli Molise Hospital, Largo Gemelli 1, Campobasso, 86100, ITALY
| | - Maurizio Craus
- Gemelli Molise Hospital, Largo Gemelli 1, Campobasso, 86100, ITALY
| | | | - Milly Buwenge
- IRCCS Azienda Ospedaliero-Universitaria di Bologna Policlinico S Orsola-Malpighi, Via Giuseppe Massarenti, 9, Bologna, Emilia-Romagna, 40138, ITALY
| | - Luca Indovina
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Via della Pineta Sacchetti, 217, Roma, Lazio, 00168, ITALY
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Via della Pineta Sacchetti, 217, Roma, Lazio, 00168, ITALY
| | - Alessio Giuseppe Morganti
- IRCCS Azienda Ospedaliero-Universitaria di Bologna Policlinico S Orsola-Malpighi, Via Giuseppe Massarenti, 9, Bologna, Emilia-Romagna, 40138, ITALY
| | - Francesco Deodato
- Gemelli Molise Hospital, Largo A. Gemelli 1, Campobasso, 86100, ITALY
| | - Savino Cilla
- Medical Physics Unit, Gemelli Molise Hospital, Largo A. Gemelli 1, Campobasso, 86100, ITALY
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14
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Ge C, Wang H, Chen K, Sun W, Li H, Shi Y. Effect of plan complexity on the dosimetry, delivery accuracy, and interplay effect in lung VMAT SBRT with 6 MV FFF beam. Strahlenther Onkol 2022; 198:744-751. [PMID: 35486127 DOI: 10.1007/s00066-022-01940-3] [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: 12/30/2021] [Accepted: 03/30/2022] [Indexed: 11/30/2022]
Abstract
PURPOSE The purpose of this study is to investigate the effect of plan complexity on the dosimetry, delivery accuracy, and interplay effect in lung stereotactic body radiation therapy (SBRT) using volumetric modulated arc therapy (VMAT) with 6 MV flattening-filter-free (FFF) beam. METHODS Twenty patients with early stage non-small cell lung cancer were included. For each patient, high-complexity (HC) and low-complexity (LC) three-partial-arc VMAT plans were optimized by adjusting the normal tissue objectives and the maximum monitoring units (MUs) for a Varian TrueBeam linear accelerator (Varian Medical Systems, Palo Alto, CA, USA) using 6 MV FFF beam. The effect of plan complexity was comprehensively evaluated in three aspects: (1) The dosimetric parameters, including CI, D2cm, R50, and dose-volume parameters of organs at risk were compared. (2) The delivery accuracy was assessed by pretreatment quality assurance for two groups of plans. (3) The motion-induced dose deviation was evaluated based on point dose measurements near the tumor center by using a programmable phantom. The standard deviation (SD) and maximum dose difference of five measurements were used to quantify the interplay effect. RESULTS The dosimetry of HC and LC plans were similar except the CI (1.003 ± 0.032 and 1.026 ± 0.043, p = 0.030) and Dmax to the spinal cord (10.6 ± 3.2 and 9.9 ± 3.0, p = 0.012). The gamma passing rates were significantly higher in LC plans for all arcs (p < 0.001). The SDs of HC and LC plans ranged from 0.5-16.6% and 0.03-2.9%, respectively, under the conditions of one-field, two-field, and three-field delivery for each plan with 0.5, 1, 2, and 3 cm motion amplitudes. The maximum dose differences of HC and LC plans were 34.5% and 9.1%, respectively. CONCLUSION For lung VMAT SBRT, LC plans have a higher delivery accuracy and a lower motion-induced dose deviation with similar dosimetry compared with HC plans.
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Affiliation(s)
- Chao Ge
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China
| | - Huidong Wang
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China
| | - Kunzhi Chen
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China
| | - Wuji Sun
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China
| | - Huicheng Li
- Jilin Province FAW General Hospital, 130011, Changchun, China
| | - Yinghua Shi
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.
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15
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Chan GH, Chin LCL, Abdellatif A, Bissonnette JP, Buckley L, Comsa D, Granville D, King J, Rapley PL, Vandermeer A. Survey of patient-specific quality assurance practice for IMRT and VMAT. J Appl Clin Med Phys 2021; 22:155-164. [PMID: 34145732 PMCID: PMC8292698 DOI: 10.1002/acm2.13294] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 04/03/2021] [Accepted: 05/06/2021] [Indexed: 12/03/2022] Open
Abstract
A first‐time survey across 15 cancer centers in Ontario, Canada, on the current practice of patient‐specific quality assurance (PSQA) for intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) delivery was conducted. The objectives were to assess the current state of PSQA practice, identify areas for potential improvement, and facilitate the continued improvement in standardization, consistency, efficacy, and efficiency of PSQA regionally. The survey asked 40 questions related to PSQA practice for IMRT/VMAT delivery. The questions addressed PSQA policy and procedure, delivery log evaluation, instrumentation, measurement setup and methodology, data analysis and interpretation, documentation, process, failure modes, and feedback. The focus of this survey was on PSQA activities related to routine IMRT/VMAT treatments on conventional linacs, including stereotactic body radiation therapy but excluding stereotactic radiosurgery. The participating centers were instructed to submit answers that reflected the collective view or opinion of their department and represented the most typical process practiced. The results of the survey provided a snapshot of the current state of PSQA practice in Ontario and demonstrated considerable variations in the practice. A large majority (80%) of centers performed PSQA measurements on all VMAT plans. Most employed pseudo‐3D array detectors with a true composite (TC) geometry. No standard approach was found for stopping or reducing frequency of measurements. The sole use of delivery log evaluation was not widely implemented, though most centers expressed interest in adopting this technology. All used the Gamma evaluation method for analyzing PSQA measurements; however, no universal approach was reported on how Gamma evaluation and pass determination criteria were determined. All or some PSQA results were reviewed regularly in two‐thirds of the centers. Planning related issues were considered the most frequent source for PSQA failures (40%), whereas the most frequent course of action for a failed PSQA was to review the result and decide whether to proceed to treatment.
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Affiliation(s)
- Gordon H Chan
- Department of Medical Physics, Juravinski Cancer Centre, Hamilton, Ontario, Canada
| | - Lee C L Chin
- Department of Medical Physics, Odette Cancer Centre, Toronto, Ontario, Canada
| | - Ady Abdellatif
- Department of Medical Physics, R.S. McLaughlin Durham Regional Cancer Centre, Oshawa, Ontario, Canada
| | - Jean-Pierre Bissonnette
- Department of Medical Physics, Princess Margaret Cancer Centre-UHN, Toronto, Ontario, Canada
| | - Lesley Buckley
- Department of Medical Physics, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Daria Comsa
- Radiation Physics Department, Southlake Regional Cancer Centre, Newmarket, Ontario, Canada
| | - Dal Granville
- Department of Medical Physics, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Jenna King
- Radiation Oncology Physics, Simcoe Muskoka Regional Cancer Centre, Barrie, Ontario, Canada
| | - Patrick L Rapley
- Medical Physics Department, Thunder Bay Regional Health Sciences Centre, Thunder Bay, Ontario, Canada
| | - Aaron Vandermeer
- Department of Medical Physics, R.S. McLaughlin Durham Regional Cancer Centre, Oshawa, Ontario, Canada
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