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Tsai WT, Hsieh HL, Hung SK, Zeng CF, Lee MF, Lin PH, Lin CY, Li WC, Chiou WY, Wu TH. Dosimetry and efficiency comparison of knowledge-based and manual planning using volumetric modulated arc therapy for craniospinal irradiation. Radiol Oncol 2024; 58:289-299. [PMID: 38452341 PMCID: PMC11165983 DOI: 10.2478/raon-2024-0018] [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: 11/04/2023] [Accepted: 01/03/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND Craniospinal irradiation (CSI) poses a challenge to treatment planning due to the large target, field junction, and multiple organs at risk (OARs) involved. The aim of this study was to evaluate the performance of knowledge-based planning (KBP) in CSI by comparing original manual plans (MP), KBP RapidPlan initial plans (RPI), and KBP RapidPlan final plans (RPF), which received further re-optimization to meet the dose constraints. PATIENTS AND METHODS Dose distributions in the target were evaluated in terms of coverage, mean dose, conformity index (CI), and homogeneity index (HI). The dosimetric results of OARs, planning time, and monitor unit (MU) were evaluated. RESULTS All MP and RPF plans met the plan goals, and 89.36% of RPI plans met the plan goals. The Wilcoxon tests showed comparable target coverage, CI, and HI for the MP and RPF groups; however, worst plan quality was demonstrated in the RPI plans than in MP and RPF. For the OARs, RPF and RPI groups had better dosimetric results than the MP group (P < 0.05 for optic nerves, eyes, parotid glands, and heart). The planning time was significantly reduced by the KBP from an average of 677.80 min in MP to 227.66 min (P < 0.05) and 307.76 min (P < 0.05) in RPI, and RPF, respectively. MU was not significantly different between these three groups. CONCLUSIONS The KBP can significantly reduce planning time in CSI. Manual re-optimization after the initial KBP is recommended to enhance the plan quality.
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Affiliation(s)
- Wei-Ta Tsai
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Radiation Oncology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
| | - Hui-Ling Hsieh
- Department of Radiation Oncology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
| | - Shih-Kai Hung
- Department of Radiation Oncology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
- School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Chi-Fu Zeng
- Department of Radiation Oncology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
| | - Ming-Fen Lee
- Department of Radiation Oncology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
| | - Po-Hao Lin
- Department of Radiation Oncology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
| | - Chia-Yi Lin
- Department of Radiation Oncology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
| | - Wei-Chih Li
- Departments of Radiation Oncology, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan
| | - Wen-Yen Chiou
- Department of Radiation Oncology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
- School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Tung-Hsin Wu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
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Kaderka R, Dogan N, Jin W, Bossart E. Effects of model size and composition on quality of head-and-neck knowledge-based plans. J Appl Clin Med Phys 2024; 25:e14168. [PMID: 37798910 PMCID: PMC10860434 DOI: 10.1002/acm2.14168] [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: 02/22/2023] [Revised: 08/23/2023] [Accepted: 09/15/2023] [Indexed: 10/07/2023] Open
Abstract
PURPOSE Knowledge-based planning (KBP) aims to automate and standardize treatment planning. New KBP users are faced with many questions: How much does model size matter, and are multiple models needed to accommodate specific physician preferences? In this study, six head-and-neck KBP models were trained to address these questions. METHODS The six models differed in training size and plan composition: The KBPFull (n = 203 plans), KBP101 (n = 101), KBP50 (n = 50), and KBP25 (n = 25) were trained with plans from two head-and-neck physicians. KBPA and KBPB each contained n = 101 plans from only one physician, respectively. An independent set of 39 patients treated to 6000-7000 cGy by a third physician was re-planned with all KBP models for validation. Standard head-and-neck dosimetric parameters were used to compare resulting plans. KBPFull plans were compared to the clinical plans to evaluate overall model quality. Additionally, clinical and KBPFull plans were presented to another physician for blind review. Dosimetric comparison of KBPFull against KBP101 , KBP50 , and KBP25 investigated the effect of model size. Finally, KBPA versus KBPB tested whether training KBP models on plans from one physician only influences the resulting output. Dosimetric differences were tested for significance using a paired t-test (p < 0.05). RESULTS Compared to manual plans, KBPFull significantly increased PTV Low D95% and left parotid mean dose but decreased dose cochlea, constrictors, and larynx. The physician preferred the KBPFull plan over the manual plan in 20/39 cases. Dosimetric differences between KBPFull , KBP101 , KBP50 , and KBP25 plans did not exceed 187 cGy on aggregate, except for the cochlea. Further, average differences between KBPA and KBPB were below 110 cGy. CONCLUSIONS Overall, all models were shown to produce high-quality plans. Differences between model outputs were small compared to the prescription. This indicates only small improvements when increasing model size and minimal influence of the physician when choosing treatment plans for training head-and-neck KBP models.
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Affiliation(s)
- Robert Kaderka
- Department of Radiation OncologyUniversity of Miami Miller School of MedicineMiamiFloridaUSA
| | - Nesrin Dogan
- Department of Radiation OncologyUniversity of Miami Miller School of MedicineMiamiFloridaUSA
| | - William Jin
- Department of Radiation OncologyUniversity of Miami Miller School of MedicineMiamiFloridaUSA
| | - Elizabeth Bossart
- Department of Radiation OncologyUniversity of Miami Miller School of MedicineMiamiFloridaUSA
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Ueda Y, Fukunaga JI, Kamima T, Shimizu Y, Kubo K, Doi H, Monzen H. Standardization of knowledge-based volumetric modulated arc therapy planning with a multi-institution model (broad model) to improve prostate cancer treatment quality. Phys Eng Sci Med 2023; 46:1091-1100. [PMID: 37247102 DOI: 10.1007/s13246-023-01278-9] [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: 12/14/2022] [Accepted: 05/08/2023] [Indexed: 05/30/2023]
Abstract
PURPOSE To evaluate whether knowledge-based volumetric modulated arc therapy plans for prostate cancer with a multi-institution model (broad model) are clinically useful and effective as a standardization method. METHODS A knowledge-based planning (KBP) model was trained with 561 prostate VMAT plans from five institutions with different contouring and planning policies. Five clinical plans at each institution were reoptimized with the broad and single institution model, and the dosimetric parameters and relationship between Dmean and the overlapping volume (rectum or bladder and target) were compared. RESULTS The differences between the broad and single institution models in the dosimetric parameters for V50, V80, V90, and Dmean were: rectum; 9.5% ± 10.3%, 3.3% ± 1.5%, 1.7% ± 1.6%, and 3.6% ± 3.6%, (p < 0.001), bladder; 8.7% ± 12.8%, 1.5% ± 2.6%, 0.7% ± 2.4%, and 2.7% ± 4.6% (p < 0.02), respectively. The differences between the broad model and clinical plans were: rectum; 2.4% ± 4.6%, 1.7% ± 1.7%, 0.7% ± 2.4%, and 1.5% ± 2.0%, (p = 0.004, 0.015, 0.112, and 0.009) bladder; 2.9% ± 5.8%, 1.6% ± 1.9%, 0.9% ± 1.7%, and 1.1% ± 4.8%, (p < 0.018), respectively. Positive values indicate that the broad model has a lower value. Strong correlations were observed (p < 0.001) in the relationship between Dmean and the rectal and bladder volume overlapping with the target in the broad model (R = 0.815 and 0.891, respectively). The broad model had the smallest R2 of the three plans. CONCLUSIONS KBP with the broad model is clinically effective and applicable as a standardization method at multiple institutions.
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Affiliation(s)
- Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69, Otemae, Chuo-ku, Osaka, 537-8567, Japan.
| | - Jun-Ichi Fukunaga
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, 3-1-1, Maidashi, Higashi- ku, Fukuoka, 812-8582, Japan
| | - Tatsuya Kamima
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan
| | - Yumiko Shimizu
- Department of Radiology, Seirei Hamamatsu General Hospital, 2-12-12 Sumiyoshi, Naka Ward, Hamamatsu, Shizuoka, 430-8558, Japan
| | - Kazuki Kubo
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Hiroshi Doi
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan
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Fanou AM, Patatoukas G, Chalkia M, Kollaros N, Kougioumtzopoulou A, Kouloulias V, Platoni K. Implementation, Dosimetric Assessment, and Treatment Validation of Knowledge-Based Planning (KBP) Models in VMAT Head and Neck Radiation Oncology. Biomedicines 2023; 11:biomedicines11030762. [PMID: 36979740 PMCID: PMC10045933 DOI: 10.3390/biomedicines11030762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 02/21/2023] [Accepted: 02/26/2023] [Indexed: 03/06/2023] Open
Abstract
The aim of this study was to evaluate knowledge-based treatment planning (KBP) models in terms of their dosimetry and deliverability and to investigate their clinical benefits. Three H&N KBP models were built utilizing RapidPlan™, based on the dose prescription, which is given according to the planning target volume (PTV). The training set for each model consisted of 43 clinically acceptable volumetric modulated arc therapy (VMAT) plans. Model quality was assessed and compared to the delivered treatment plans using the homogeneity index (HI), conformity index (CI), structure dose difference (PTV, organ at risk—OAR), monitor units, MU factor, and complexity index. Model deliverability was assessed through a patient-specific quality assurance (PSQA) gamma index-based analysis. The dosimetric assessment showed better OAR sparing for the RapidPlan™ plans and for the low- and high-risk PTV, and the HI, and CI were comparable between the clinical and RapidPlan™ plans, while for the intermediate-risk PTV, CI was better for clinical plans. The 2D gamma passing rates for RapidPlan™ plans were similar or better than the clinical ones using the 3%/3 mm gamma-index criterion. Monitor units, the MU factors, and complexity indices were found to be comparable between RapidPlan™ and the clinical plans. Knowledge-based treatment plans can be safely adapted into clinical routines, providing improved plan quality in a time efficient way while minimizing user variability.
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Affiliation(s)
- Anna-Maria Fanou
- Medical Physics Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
- Correspondence: (A.-M.F.); (K.P.)
| | - Georgios Patatoukas
- Medical Physics Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
| | - Marina Chalkia
- Medical Physics Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
| | - Nikolaos Kollaros
- Medical Physics Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
| | - Andromachi Kougioumtzopoulou
- Radiation Therapy Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
| | - Vassilis Kouloulias
- Radiation Therapy Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
| | - Kalliopi Platoni
- Medical Physics Unit, Second Department of Radiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Haidari, 12462 Athens, Greece
- Correspondence: (A.-M.F.); (K.P.)
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Mashayekhi M, McBeth R, Nguyen D, Yen A, Trivedi Z, Moon D, Avkshtol V, Vo D, Sher D, Jiang S, Lin MH. Artificial Intelligence Guided Physician Directive Improves Head and Neck Planning Quality and Practice Uniformity: A Prospective Study. Clin Transl Radiat Oncol 2023; 40:100616. [PMID: 36968578 PMCID: PMC10034417 DOI: 10.1016/j.ctro.2023.100616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 02/20/2023] [Accepted: 03/07/2023] [Indexed: 03/12/2023] Open
Abstract
•AI dose predictor was fully integrated with treatment planning system and used as a physicain decision support tool to improve uniformity of practice.•Model was trained based on our standard of practice, but implemented at the time of expansion with 3 new physicians join the practice.•Phase 1 retrospective evaluation demonstrated the non-uniform practice among 3 MDs and only 52.9% frequency planner can achieve physicians' directives.•Significant improvement in practice uniformity of practice was observed after utilizing AI as DST and 80.4% frequency clinical plan can achieve AI-guided physician directives.
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Affiliation(s)
- Maryam Mashayekhi
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Rafe McBeth
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Allen Yen
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | | | - Dominic Moon
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Vlad Avkshtol
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Dat Vo
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - David Sher
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Mu-Han Lin
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
- Corresponding author at: 2280 Inwood Rd, Dallas, TX 75390, USA.
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Sagawa T, Ueda Y, Tsuru H, Kamima T, Ohira S, Tamura M, Miyazaki M, Monzen H, Konishi K. Dosimetric potential of knowledge-based planning model trained with HyperArc plans for brain metastases. J Appl Clin Med Phys 2022; 24:e13836. [PMID: 36333969 PMCID: PMC9924102 DOI: 10.1002/acm2.13836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/07/2022] [Accepted: 10/13/2022] [Indexed: 11/07/2022] Open
Abstract
OBJECTIVE Dosimetric potential of knowledge-based RapidPlan planning model trained with HyperArc plans (Model-HA) for brain metastases has not been reported. We developed a Model-HA and compared its performance with that of clinical volumetric modulated arc therapy (VMAT) plans. METHODS From 67 clinical stereotactic radiosurgery (SRS) HyperArc plans for brain metastases, 47 plans were used to build and train a Model-HA. The other 20 clinical HyperArc plans were recalculated in RapidPlan system with Model-HA. The model performance was validated with the 20 plans by comparing dosimetric parameters for normal brain tissue between clinical plans and model-generated plans. The 20 clinical conventional VMAT-based SRS or stereotactic radiotherapy plans (CL-VMAT) were reoptimized with Model-HA (RP) and HyperArc system (HA), respectively. The dosimetric parameters were compared among three plans (CL-VMAT vs. RP vs. HA) in terms of planning target volume (PTV), normal brain excluding PTVs (Brain - PTV), brainstem, chiasm, and both optic nerves. RESULTS In model validation, the optimization performance of Model-HA was comparable to that of HyperArc system. In comparison to CL-VMAT, there were no significant differences among three plans with respect to PTV coverage (p > 0.17) and maximum dose for brainstem, chiasm, and optic nerves (p > 0.40). RP provided significantly lower V20 Gy , V12 Gy , and V4 Gy for Brain - PTV than CL-VMAT (p < 0.01). CONCLUSION The Model-HA has the potential to significantly reduce the normal brain dose of the original VMAT plans for brain metastases.
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Affiliation(s)
- Tomohiro Sagawa
- Department of Radiation OncologyOsaka International Cancer InstituteOsakaJapan
| | - Yoshihiro Ueda
- Department of Radiation OncologyOsaka International Cancer InstituteOsakaJapan
| | - Haruhi Tsuru
- Department of Medical Physics and EngineeringGraduate School of MedicineOsaka UniversitySuitaJapan
| | - Tatsuya Kamima
- Radiation Oncology DepartmentCancer Institute HospitalJapanese Foundation for Cancer ResearchTokyoJapan
| | - Shingo Ohira
- Department of Radiation OncologyOsaka International Cancer InstituteOsakaJapan
| | - Mikoto Tamura
- Department of Medical PhysicsGraduate School of Medical SciencesKindai UniversitySayamaJapan
| | - Masayoshi Miyazaki
- Department of Radiation OncologyOsaka International Cancer InstituteOsakaJapan
| | - Hajime Monzen
- Department of Medical PhysicsGraduate School of Medical SciencesKindai UniversitySayamaJapan
| | - Koji Konishi
- Department of Radiation OncologyOsaka International Cancer InstituteOsakaJapan
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Ahn KH, Rondelli D, Koshy M, Partouche JA, Hasan Y, Liu H, Yenice K, Aydogan B. Knowledge-based planning for multi-isocenter VMAT total marrow irradiation. Front Oncol 2022; 12:942685. [PMID: 36267964 PMCID: PMC9577613 DOI: 10.3389/fonc.2022.942685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 09/20/2022] [Indexed: 12/04/2022] Open
Abstract
Purpose Total marrow irradiation (TMI) involves optimization of extremely large target volumes and requires extensive clinical experience and time for both treatment planning and delivery. Although volumetric modulated arc therapy (VMAT) achieves substantial reduction in treatment delivery time, planning process still presents a challenge due to use of multiple isocenters and multiple overlapping arcs. We developed and evaluated a knowledge-based planning (KBP) model for VMAT-TMI to address these clinical challenges. Methods Fifty-one patients previously treated in our clinic were selected for the model training, while 22 patients from another clinic were used as a test set. All plans used a 3-isocenter to cover sub-target volumes of head and neck (HN), chest, and pelvis. Chest plan was performed first and then used as the base dose for both the HN and pelvis plans to reduce hot spots around the field junctions. This resulted in a wide range of dose-volume histograms (DVH). To address this, plans without the base-dose plan were optimized and added to the library to train the model. Results KBP achieved our clinical goals (95% of PTV receives 100% of Rx) in a single day, which used to take 4-6 days of effort without KBP. Statistically significant reductions with KBP were observed in the mean dose values to brain, lungs, oral cavity and lenses. KBP substantially improved 105% dose spillage (14.1% ± 2.4% vs 31.8% ± 3.8%), conformity index (1.51 ± 0.06 vs 1.81 ± 0.12) and homogeneity index (1.25 ± 0.02 vs 1.33 ± 0.03). Conclusions KBP improved dosimetric performance with uniform quality. It reduced dependence on planner experience and achieved a factor of 5 reduction in planning time to produce quality plans to allow its wide-spread clinical implementation.
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Affiliation(s)
- Kang-Hyun Ahn
- Department of Radiation Oncology, University of Illinois, Chicago, IL, United States
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States
| | - Damiano Rondelli
- Division of Hematology/Oncology, University of Illinois, Chicago, IL, United States
| | - Matthew Koshy
- Department of Radiation Oncology, University of Illinois, Chicago, IL, United States
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States
| | - Julien A. Partouche
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States
| | - Yasmin Hasan
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States
| | - Hongtao Liu
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States
| | - Kamil Yenice
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States
| | - Bulent Aydogan
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States
- *Correspondence: Bulent Aydogan,
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Tudda A, Castriconi R, Benecchi G, Cagni E, Cicchetti A, Dusi F, Esposito PG, Guernieri M, Ianiro A, Landoni V, Mazzilli A, Moretti E, Oliviero C, Placidi L, Rambaldi Guidasci G, Rancati T, Scaggion A, Trojani V, Fiorino C. Knowledge-based multi-institution plan prediction of whole breast irradiation with tangential fields. Radiother Oncol 2022; 175:10-16. [PMID: 35868603 DOI: 10.1016/j.radonc.2022.07.012] [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: 03/14/2022] [Revised: 07/07/2022] [Accepted: 07/09/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE To quantify inter-institute variability of Knowledge-Based (KB) models for right breast cancer patients treated with tangential fields whole breast irradiation (WBI). MATERIALS AND METHODS Ten institutions set KB models by using RapidPlan (Varian Inc.), following previously shared methodologies. Models were tested on 20 new patients from the same institutes, exporting DVH predictions of heart, ipsilateral lung, contralateral lung, and contralateral breast. Inter-institute variability was quantified by the inter-institute SDint of predicted DVHs/Dmean. Association between lung sparing vs PTV coverage strategy was also investigated. The transferability of models was evaluated by the overlap of each model's geometric Principal Component (PC1) when applied to the test patients of the other 9 institutes. RESULTS The overall inter-institute variability of DVH/Dmean ipsilateral lung dose prediction, was less than 2% (20%-80% dose range) and 0.55 Gy respectively (1SD) for a 40 Gy in 15 fraction schedule; it was < 0.2 Gy for other OARs. Institute 6 showed the lowest mean dose prediction value and no overlap between PTV and ipsilateral lung. Once excluded, the predicted ipsilateral lung Dmean was correlated with median PTV D99% (R2 = 0.78). PC1 values were always within the range of applicability (90th percentile) for 7 models: for 2 models they were outside in 1/18 cases. For the model of institute 6, it failed in 7/18 cases. The impact of inter-institute variability of dose calculation was tested and found to be almost negligible. CONCLUSIONS Results show limited inter-institute variability of plan prediction models translating in high inter-institute interchangeability, except for one of ten institutes. These results encourage future investigations in generating benchmarks for plan prediction incorporating inter-institute variability.
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Affiliation(s)
- Alessia Tudda
- Medical Physics Dept, San Raffaele Scientific Institute, Milano, Italy; Università Statale di Milano, Milano, Italy
| | | | | | - Elisabetta Cagni
- Medical Physics Unit, Department of Advanced Technology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | | | - Francesca Dusi
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | | | - Marika Guernieri
- Department of Medical Physics, University Hospital, Udine, Italy
| | - Anna Ianiro
- Istituto Nazionale dei Tumori Regina Elena, Rome, Italy
| | | | - Aldo Mazzilli
- Medical Physics Dept, University Hospital of Parma AOUP, Italy
| | - Eugenia Moretti
- Department of Medical Physics, University Hospital, Udine, Italy
| | | | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Giulia Rambaldi Guidasci
- Amethyst Radioterapia Italia, Medical Physics Department, San Giovanni Calibita Fatebenefratelli Hospital, Rome, Italy
| | - Tiziana Rancati
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Alessandro Scaggion
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Valeria Trojani
- Medical Physics Unit, Department of Advanced Technology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Claudio Fiorino
- Medical Physics Dept, San Raffaele Scientific Institute, Milano, Italy
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Development and Clinical Implementation of an Automated Virtual Integrative Planner for Radiation Therapy of Head and Neck Cancer. Adv Radiat Oncol 2022; 8:101029. [PMID: 36578278 PMCID: PMC9791598 DOI: 10.1016/j.adro.2022.101029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 07/10/2022] [Indexed: 12/31/2022] Open
Abstract
Purpose Head and neck (HN) radiation (RT) treatment planning is complex and resource intensive. Deviations and inconsistent plan quality significantly affect clinical outcomes. We sought to develop a novel automated virtual integrative (AVI) knowledge-based planning application to reduce planning time, increase consistency, and improve baseline quality. Methods and Materials An in-house write-enabled script was developed from a library of 668 previously treated HN RT plans. Prospective hazard analysis was performed, and mitigation strategies were implemented before clinical release. The AVI-planner software was retrospectively validated in a cohort of 52 recent HN cases. A physician panel evaluated planning limitations during initial deployment, and feedback was enacted via software refinements. A final second set of plans was generated and evaluated. Kolmogorov-Smirnov test in addition to generalized evaluation metric and weighted experience score were used to compare normal tissue sparing between final AVI planner versus respective clinically treated and historically accepted plans. A t test was used to compare the interactive time, complexity, and monitor units for AVI planner versus manual optimization. Results Initially, 86% of plans were acceptable to treat, with 10% minor and 4% major revisions or rejection recommended. Variability was noted in plan quality among HN subsites, with high initial quality for oropharynx and oral cavity plans. Plans needing revisions were comprised of sinonasal, nasopharynx, P-16 negative squamous cell carcinoma unknown primary, or cutaneous primary sites. Normal tissue sparing varied within subsites, but AVI planner significantly lowered mean larynx dose (median, 18.5 vs 19.7 Gy; P < .01) compared with clinical plans. AVI planner significantly reduced interactive optimization time (mean, 2 vs 85 minutes; P < .01). Conclusions AVI planner reliably generated clinically acceptable RT plans for oral cavity, salivary, oropharynx, larynx, and hypopharynx cancers. Physician-driven iterative learning processes resulted in favorable evolution in HN RT plan quality with significant time savings and improved consistency using AVI planner.
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Mashayekhi M, Tapia IR, Balagopal A, Zhong X, Barkousaraie AS, McBeth R, Lin MH, Jiang S, Nguyen D. Site-agnostic 3D dose distribution prediction with deep learning neural networks. Med Phys 2022; 49:1391-1406. [PMID: 35037276 PMCID: PMC9870295 DOI: 10.1002/mp.15461] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 11/23/2021] [Accepted: 12/20/2021] [Indexed: 01/26/2023] Open
Abstract
PURPOSE Typically, the current dose prediction models are limited to small amounts of data and require retraining for a specific site, often leading to suboptimal performance. We propose a site-agnostic, three-dimensional dose distribution prediction model using deep learning that can leverage data from any treatment site, thus increasing the total data available to train the model. Applying our proposed model to a new target treatment site requires only a brief fine-tuning of the model to the new data and involves no modifications to the model input channels or its parameters. Thus, it can be efficiently adapted to a different treatment site, even with a small training dataset. METHODS This study uses two separate datasets/treatment sites: data from patients with prostate cancer treated with intensity-modulated radiation therapy (source data), and data from patients with head-and-neck cancer treated with volumetric-modulated arc therapy (target data). We first developed a source model with 3D UNet architecture, trained from random initial weights on the source data. We evaluated the performance of this model on the source data. We then studied the generalizability of the model to the new target dataset via transfer learning. To do this, we built three more models, all with the same 3D UNet architecture: target model, adapted model, and combined model. The source and target models were trained on the source and target data from random initial weights, respectively. The adapted model fine-tuned the source model to the target domain by using the target data. Finally, the combined model was trained from random initial weights on a combined data pool consisting of both target and source datasets. We tested all four models on the target dataset and evaluated quantitative dose-volume histogram metrics for the planning target volume (PTV) and organs at risk (OARs). RESULTS When tested on the source treatment site, the source model accurately predicted the dose distributions with average (mean, max) absolute dose errors of (0.32%±0.14, 2.37%±0.93) (PTV) relative to the prescription dose, and highest mean dose error of 1.68%±0.76, and highest max dose error of 5.47%± 3.31 for femoral head right. The error in PTV dose coverage prediction is 3.21%±1.51 for D98 , 3.04%±1.69 for D95 , and 1.83%±1.01 for D02 . Averaging across all OARs, the source model predicted the OAR mean dose within 1.38% and the OAR max dose within 3.64%. For the target treatment site, the target model average (mean, max) absolute dose errors relative to the prescription dose for the PTV were (1.08%±0.95, 2.90%±1.35). Left cochlea had the highest mean and max dose errors of 5.37%±5.82 and 8.33%±8.88, respectively. The errors in PTV dose coverage prediction for D98 and D95 were 2.88%±1.59 and 2.55%±1.28, respectively. The target model can predict the OAR mean dose within 2.43% and the OAR max dose within 4.33% on average across all OARs. CONCLUSION We developed a site-agnostic model for three-dimensional dose prediction and tested its adaptability to a new target treatment site via transfer learning. Our proposed model can make accurate predictions with limited training data.
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Affiliation(s)
- Maryam Mashayekhi
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Itzel Ramirez Tapia
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Anjali Balagopal
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Xinran Zhong
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Azar Sadeghnejad Barkousaraie
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Rafe McBeth
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Mu-Han Lin
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Steve Jiang
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
- Author to whom any correspondence should be addressed.
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11
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Pokharel S, Pacheco A, Tanner S. Assessment of efficacy in automated plan generation for Varian Ethos intelligent optimization engine. J Appl Clin Med Phys 2022; 23:e13539. [PMID: 35084090 PMCID: PMC8992949 DOI: 10.1002/acm2.13539] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 12/29/2021] [Accepted: 01/09/2022] [Indexed: 11/19/2022] Open
Abstract
Varian Ethos, a new treatment platform, is capable of automatically generating treatment plans for initial planning and for online, adaptive planning, using an intelligent optimization engine (IOE). The primary purpose of this study is to assess the efficacy of Varian Ethos IOE for auto‐planning and intercompare different treatment modalities within the Ethos treatment planning system (TPS). A total of 36 retrospective prostate and proximal seminal vesicles cases were selected for this study. The prescription dose was 50.4 Gy in 28 fractions to the proximal seminal vesicles, with a simultaneous integrated boost of 70 Gy to the prostate gland. Based on RT intent, three treatment plans were auto‐generated in the Ethos TPS and were exported to the Eclipse TPS for intercomparison with the Eclipse treatment plan. When normalized for the same planning target volume (PTV) coverage, Ethos plans Dmax% were 108.1 ± 1.2%, 108.4 ± 1.6%, and 109.6 ± 2.0%, for the 9‐field IMRT, 12‐field IMRT, and 2‐full arc VMAT plans, respectively. This compared well with Eclipse plan Dmax% values, which was 108.8 ± 1.4%. OAR indices were also evaluated for Ethos plans using Radiation Therapy Oncology Group report 0415 as a guide and were found to be comparable to each other and the Eclipse plans. While all Ethos plans were comparable, we found that, in general, the Ethos 12‐field IMRT plans met most of the dosimetric goals for treatment. Also, Ethos IOE consistently generated dosimetrically hotter VMAT plans versus IMRT plans. On average, Ethos TPS took 13 min to generate 2‐full arc VMAT plans, compared to 5 min for 12‐field IMRT plans. Varian Ethos TPS can generate multiple treatment plans in an efficient time frame and the quality of the plans could be deemed clinically acceptable when compared to manually generated treatment plans.
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Affiliation(s)
- Shyam Pokharel
- Department of Radiation Oncology, GenesisCare, Naples, Florida, USA.,Department of Radiation Oncology, Boca Raton Regional Hospital, Baptist Health South Florida, Lynn Cancer Institute, Boca Raton, Florida, USA
| | - Abilio Pacheco
- Department of Radiation Oncology, GenesisCare, Naples, Florida, USA
| | - Suzanne Tanner
- Department of Radiation Oncology, GenesisCare, Naples, Florida, USA
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12
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Frizzelle M, Pediaditaki A, Thomas C, South C, Vanderstraeten R, Wiessler W, Adams E, Jagadeesan S, Lalli N. Using multi-centre data to train and validate a knowledge-based model for planning radiotherapy of the head and neck. Phys Imaging Radiat Oncol 2022; 21:18-23. [PMID: 35391782 PMCID: PMC8981763 DOI: 10.1016/j.phro.2022.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 01/12/2022] [Accepted: 01/12/2022] [Indexed: 10/28/2022] Open
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13
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Siciarz P, Alfaifi S, Uytven EV, Rathod S, Koul R, McCurdy B. Machine learning for dose-volume histogram based clinical decision-making support system in radiation therapy plans for brain tumors. Clin Transl Radiat Oncol 2021; 31:50-57. [PMID: 34632117 PMCID: PMC8487981 DOI: 10.1016/j.ctro.2021.09.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 08/27/2021] [Accepted: 09/06/2021] [Indexed: 11/17/2022] Open
Abstract
Extraction, analysis, and interpretation of historical treatment planning data is valuable but very time-consuming. Proposed machine learning model classifies radiotherapy plans based on their treatment planning objectives and trade-offs. Application of double nested cross-validation enabled to build a robust model that achieved 94% accuracy on a testing data. Model reasoning investigated with SHAP values showed consistency with clinical observations.
Purpose To create and investigate a novel, clinical decision-support system using machine learning (ML). Methods and Materials The ML model was developed based on 79 radiotherapy plans of brain tumor patients that were prescribed a total dose of 60 Gy delivered with volumetric-modulated arc therapy (VMAT). Structures considered for analysis included planning target volume (PTV), brainstem, cochleae, and optic chiasm. The model aimed to classify the target variable that included class-0 corresponding to plans for which the PTV treatment planning objective was met and class-1 that was associated with plans for which the PTV objective was not met due to the priority trade-off to meet one or more organs-at-risk constraints. Several models were evaluated using double-nested cross-validation and an area-under-the-curve (AUC) metric, with the highest performing one selected for further investigation. The model predictions were explained with Shapely additive explanation (SHAP) interaction values. Results The highest-performing model was Logistic Regression achieving an accuracy of 93.8 ± 4.1% and AUC of 0.98 ± 0.02 on the testing data. The SHAP analysis indicated that the ΔD99% metric for PTV had the greatest influence on the model predictions. The least important feature was ΔDMAX for the left and right cochleae. Conclusions The trained model achieved satisfactory accuracy and can be used by medical physicists in a data-driven quality assurance program as well as by radiation oncologists to support their decision-making process in terms of treatment plan approval and potential plan modifications. Model explanation analysis showed that the model relies on clinically valid logic when making predictions.
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Affiliation(s)
- Pawel Siciarz
- Department of Medical Physics, CancerCare Manitoba, 675 McDermot Avenue, Winnipeg, MB R3E 0V9, Canada
- Department of Physics and Astronomy, University of Manitoba, Allen Building, Winnipeg, MB R3T 2N2, Canada
- Corresponding author at: Department of Medical Physics, CancerCare Manitoba, 675 McDermot Avenue, Winnipeg, MB R3E 0V9, Canada.
| | - Salem Alfaifi
- Radiation Oncology Resident, Department of Radiation Oncology, CancerCare Manitoba, 675 McDermot Avenue, Winnipeg, MB R3E 0V9, Canada
| | - Eric Van Uytven
- Radiation Oncology Resident, Department of Radiation Oncology, CancerCare Manitoba, 675 McDermot Avenue, Winnipeg, MB R3E 0V9, Canada
| | - Shrinivas Rathod
- Radiation Oncology Resident, Department of Radiation Oncology, CancerCare Manitoba, 675 McDermot Avenue, Winnipeg, MB R3E 0V9, Canada
- Department of Radiology, University of Manitoba, GA216-820 Sherbrook Street, Winnipeg, MB R3T 2N2, Canada
| | - Rashmi Koul
- Department of Radiology, University of Manitoba, GA216-820 Sherbrook Street, Winnipeg, MB R3T 2N2, Canada
- Medical Director and Head, Radiation Oncology Program, Department of Radiation Oncology, CancerCare Manitoba, 675 McDermot Avenue, Winnipeg, MB R3E 0V9, Canada
| | - Boyd McCurdy
- Department of Physics and Astronomy, University of Manitoba, Allen Building, Winnipeg, MB R3T 2N2, Canada
- Department of Radiology, University of Manitoba, GA216-820 Sherbrook Street, Winnipeg, MB R3T 2N2, Canada
- Head of Radiation Oncology Physics Group, Department of Medical Physics, CancerCare Manitoba, 675 McDermot Avenue, Winnipeg, MB R3E 0V9, Canada
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14
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Nitta Y, Ueda Y, Isono M, Ohira S, Masaoka A, Karino T, Inui S, Miyazaki M, Teshima T. Customization of a Model For Knowledge-Based Planning to Achieve Ideal Dose Distributions in Volume Modulated arc Therapy for Pancreatic Cancers. J Med Phys 2021; 46:66-72. [PMID: 34566285 PMCID: PMC8415244 DOI: 10.4103/jmp.jmp_76_20] [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: 08/26/2020] [Revised: 04/27/2021] [Accepted: 04/27/2021] [Indexed: 11/20/2022] Open
Abstract
Purpose: To evaluate customizing a knowledge-based planning (KBP) model using dosimetric analysis for volumetric modulated arc therapy for pancreatic cancer. Materials and Methods: The first model (M1) using 56 plans and the second model (M2) using 31 plans were created in the first 7 months of the study. The ratios of volume of both kidneys overlapping the expanded planning target volume to the total volume of both kidneys (Voverlap/Vwhole) were calculated in all cases to customize M1. Regression lines were derived from Voverlap/Vwhole and mean dose to both kidneys. The third model (M3) was created using 30 plans which data put them below the regression line. For validation, KBP was performed with the three models on 21 patients. Results: V18 of the left kidney for M1 plans was 7.3% greater than for clinical plans. Dmean of the left kidney for M2 plans was 2.2% greater than for clinical plans. There was no significant difference between all kidney doses in M3 and clinical plans. Dmean of the left kidney for M2 plans was 2.2% greater than for clinical plans. Dmean to both kidneys did not differ significantly between the three models in validation plans with Voverlap/Vwhole lower than average. In plans with larger than average volumes, the Dmean of validation plans created by M3 was significantly lower for both kidneys by 1.7 and 0.9 Gy than with M1 and M2, respectively. Conclusions: Selecting plans to register in a model by analyzing dosimetry and geometry is an effective means of improving the KBP model.
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Affiliation(s)
- Yuya Nitta
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Masaru Isono
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Shingo Ohira
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Akira Masaoka
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Tsukasa Karino
- Department of Radiology, Osaka Women's and Children's Hospital, Osaka, Japan
| | - Shoki Inui
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan.,Department of Medical Physics and Engineering, Osaka University Graduate School, Osaka, Japan
| | - Masayoshi Miyazaki
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan.,Department of Radiology, Hyogo College of Medicine, Hyogo, Japan
| | - Teruki Teshima
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
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15
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Nakamura K, Okuhata K, Tamura M, Otsuka M, Kubo K, Ueda Y, Nakamura Y, Nakamatsu K, Tanooka M, Monzen H, Nishimura Y. An updating approach for knowledge-based planning models to improve plan quality and variability in volumetric-modulated arc therapy for prostate cancer. J Appl Clin Med Phys 2021; 22:113-122. [PMID: 34338435 PMCID: PMC8425874 DOI: 10.1002/acm2.13353] [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: 03/02/2021] [Revised: 06/22/2021] [Accepted: 06/23/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE The purpose of this study was to compare the dose-volume parameters and regression scatter plots of the iteratively improved RapidPlan (RP) models, specific knowledge-based planning (KBP) models, in volumetric-modulated arc therapy (VMAT) for prostate cancer over three periods. METHODS A RP1 model was created from 47 clinical intensity-modulated radiation therapy (IMRT)/VMAT plans. A RP2 model was created to exceed dosimetric goals which set as the mean values +1SD of the dose-volume parameters of RP1 (50 consecutive new clinical VMAT plans). A RP3 model was created with more strict dose constraints for organs at risks (OARs) than RP1 and RP2 models (50 consecutive anew clinical VMAT plans). Each RP model was validated against 30 validation plans (RP1, RP2, and RP3) that were not used for model configuration, and the dose-volume parameters were compared. The Cook's distances of regression scatterplots of each model were also evaluated. RESULTS Significant differences (p < 0.05) between RP1 and RP2 were found in Dmean (101.5% vs. 101.9%), homogeneity index (3.90 vs. 4.44), 95% isodose conformity index (1.22 vs. 1.20) for the target, V40Gy (47.3% vs. 45.7%), V60Gy (27.9% vs. 27.1%), V70Gy (16.4% vs. 15.2%), and V78Gy (0.4% vs. 0.2%) for the rectal wall, and V40Gy (43.8% vs. 41.8%) and V70Gy (21.3% vs. 20.5%) for the bladder wall, whereas only V70Gy (15.2% vs. 15.8%) of the rectal wall differed significantly between RP2 and RP3. The proportions of cases with a Cook's distance of <1.0 (RP1, RP2, and RP3 models) were 55%, 78%, and 84% for the rectal wall, and 77%, 68%, and 76% for the bladder wall, respectively. CONCLUSIONS The iteratively improved RP models, reflecting the clear dosimetric goals based on the RP feedback (dose-volume parameters) and more strict dose constraints for the OARs, generated superior dose-volume parameters and the regression scatterplots in the model converged. This approach could be used to standardize the inverse planning strategies.
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Affiliation(s)
- Kenji Nakamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Japan.,Department of Radiotherapy, Takarazuka City Hospital, Kohama, Takarazuka, Japan
| | - Katsuya Okuhata
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Japan
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Japan
| | - Masakazu Otsuka
- Department of Radiology, Kindai University Hospital, Osakasayama, Japan
| | - Kazuki Kubo
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Japan
| | - Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, Chuo-ku, Japan
| | - Yasunori Nakamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Japan
| | - Kiyoshi Nakamatsu
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osakasayama, Japan
| | - Masao Tanooka
- Department of Radiotherapy, Takarazuka City Hospital, Kohama, Takarazuka, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Japan
| | - Yasumasa Nishimura
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osakasayama, Japan
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16
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Momin S, Fu Y, Lei Y, Roper J, Bradley JD, Curran WJ, Liu T, Yang X. Knowledge-based radiation treatment planning: A data-driven method survey. J Appl Clin Med Phys 2021; 22:16-44. [PMID: 34231970 PMCID: PMC8364264 DOI: 10.1002/acm2.13337] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/26/2021] [Accepted: 06/02/2021] [Indexed: 12/18/2022] Open
Abstract
This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data-driven KBP methods to dose prediction.
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Affiliation(s)
- Shadab Momin
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Yabo Fu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Jeffrey D. Bradley
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
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17
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Hirashima H, Nakamura M, Mukumoto N, Ashida R, Fujii K, Nakamura K, Nakajima A, Sakanaka K, Yoshimura M, Mizowaki T. Reducing variability among treatment machines using knowledge-based planning for head and neck, pancreatic, and rectal cancer. J Appl Clin Med Phys 2021; 22:245-254. [PMID: 34151503 PMCID: PMC8292706 DOI: 10.1002/acm2.13316] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 05/14/2021] [Accepted: 05/17/2021] [Indexed: 11/18/2022] Open
Abstract
Purpose This study aimed to assess dosimetric indices of RapidPlan model‐based plans for different energies (6, 8, 10, and 15 MV; 6‐ and 10‐MV flattening filter‐free), multileaf collimator (MLC) types (Millennium 120, High Definition 120, dual‐layer MLC), and disease sites (head and neck, pancreatic, and rectal cancer) and compare these parameters with those of clinical plans. Methods RapidPlan models in the Eclipse version 15.6 were used with the data of 28, 42, and 20 patients with head and neck, pancreatic, and rectal cancer, respectively. RapidPlan models of head and neck, pancreatic, and rectal cancer were created for TrueBeam STx (High Definition 120) with 6 MV, TrueBeam STx with 10‐MV flattening filter‐free, and Clinac iX (Millennium 120) with 15 MV, respectively. The models were used to create volumetric‐modulated arc therapy plans for a 10‐patient test dataset using all energy and MLC types at all disease sites. The Holm test was used to compare multiple dosimetric indices in different treatment machines and energy types. Results The dosimetric indices for planning target volume and organs at risk in RapidPlan model‐based plans were comparable to those in the clinical plan. Furthermore, no dose difference was observed among the RapidPlan models. The variability among RapidPlan models was consistent regardless of the treatment machines, MLC types, and energy. Conclusions Dosimetric indices of RapidPlan model‐based plans appear to be comparable to the ones based on clinical plans regardless of energies, MLC types, and disease sites. The results suggest that the RapidPlan model can generate treatment plans independent of the type of treatment machine.
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Affiliation(s)
- Hideaki Hirashima
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mitsuhiro Nakamura
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Division of Medical Physics, Department of Information Technology and Medical Engineering, Faculty of Human Health Science, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Nobutaka Mukumoto
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryo Ashida
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kota Fujii
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kiyonao Nakamura
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Aya Nakajima
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Katsuyuki Sakanaka
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Michio Yoshimura
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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18
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Wada Y, Monzen H, Tamura M, Otsuka M, Inada M, Ishikawa K, Doi H, Nakamatsu K, Nishimura Y. Dosimetric Evaluation of Simplified Knowledge-Based Plan with an Extensive Stepping Validation Approach in Volumetric-Modulated Arc Therapy-Stereotactic Body Radiotherapy for Lung Cancer. J Med Phys 2021; 46:7-15. [PMID: 34267484 PMCID: PMC8240912 DOI: 10.4103/jmp.jmp_67_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 03/05/2021] [Accepted: 03/05/2021] [Indexed: 11/18/2022] Open
Abstract
Purpose: We investigated the performance of the simplified knowledge-based plans (KBPs) in stereotactic body radiotherapy (SBRT) with volumetric-modulated arc therapy (VMAT) for lung cancer. Materials and Methods: For 50 cases who underwent SBRT, only three structures were registered into knowledge-based model: total lung, spinal cord, and planning target volume. We performed single auto-optimization on VMAT plans in two steps: 19 cases used for the model training (closed-loop validation) and 16 new cases outside of training set (open-loop validation) for TrueBeam (TB) and Halcyon (Hal) linacs. The dosimetric parameters were compared between clinical plans (CLPs) and KBPs: CLPclosed, KBPclosed-TB and KBPclosed-Hal in closed-loop validation, CLPopen, KBPopen-TB and KBPopen-Hal in open-loop validation. Results: All organs at risk were comparable between CLPs and KBPs except for contralateral lung: V5 of KBPs was approximately 3%–7% higher than that of CLPs. V20 of total lung for KBPs showed comparable to CLPs; CLPclosed vs. KBPclosed-TB and CLPclosed vs. KBPclosed-Hal: 4.36% ± 2.87% vs. 3.54% ± 1.95% and 4.36 ± 2.87% vs. 3.54% ± 1.94% (P = 0.54 and 0.54); CLPopen vs. KBPopen-TB and CLPopen vs. KBPopen-Hal: 4.18% ± 1.57% vs. 3.55% ± 1.27% and 4.18% ± 1.57% vs. 3.67% ± 1.26% (P = 0.19 and 0.27). CI95 of KBPs with both linacs was superior to that of the CLP in closed-loop validation: CLPclosed vs. KBPclosed-TB vs. KBPclosed-Hal: 1.32% ± 0.12% vs. 1.18% ± 0.09% vs. 1.17% ± 0.06% (P < 0.01); and open-loop validation: CLPopen vs. KBPopen-TB vs. KBPopen-Hal: 1.22% ± 0.09% vs. 1.14% ± 0.04% vs. 1.16% ± 0.05% (P ≤ 0.01). Conclusions: The simplified KBPs with limited number of structures and without planner intervention were clinically acceptable in the dosimetric parameters for lung VMAT-SBRT planning.
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Affiliation(s)
- Yutaro Wada
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Osaka, Japan
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Osaka, Japan
| | - Masakazu Otsuka
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osakasayama, Osaka, Japan
| | - Masahiro Inada
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Kazuki Ishikawa
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Hiroshi Doi
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Kiyoshi Nakamatsu
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Yasumasa Nishimura
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
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Inoue E, Doi H, Monzen H, Tamura M, Inada M, Ishikawa K, Nakamatsu K, Nishimura Y. Dose-volume Histogram Analysis of Knowledge-based Volumetric-modulated Arc Therapy Planning in Postoperative Breast Cancer Irradiation. In Vivo 2021; 34:1095-1101. [PMID: 32354897 DOI: 10.21873/invivo.11880] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 02/04/2020] [Accepted: 02/10/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND/AIM We evaluated the dosimetric profiles of manually generated volumetric-modulated arc therapy (VMAT) plans and performance of a commercial knowledge-based planning system (KBP) in treating breast cancer. MATERIALS AND METHODS We defined the manually generated VMAT plan as the manual plan (MP). Twenty MPs were generated for left-sided breast cancer patients who underwent breast-conserving surgery and used to develop a KBP training set. The other five patients were used for validation. The dosimetric parameters among MPs, tangential irradiation plans (TPs), and KBP-VMAT plans (KBP-Ps) were compared. RESULTS D95 and homogeneity of the planning target volume (PTV) were significantly higher and greater in MPs and KBP-Ps than in TPs. Lung V20, V40 The Dmean for the left anterior descending artery was lower in MPs and KBP-Ps than in TPs. KBP could save time in generating VMAT plans. CONCLUSION MPs and KBP-Ps could ensure higher dose uniformity of PTV than TPs. KBP could faster generate comparable MPs for breast cancer.
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Affiliation(s)
- Eri Inoue
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Hiroshi Doi
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osaka, Japan
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osaka, Japan
| | - Masahiro Inada
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Kazuki Ishikawa
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Kiyoshi Nakamatsu
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Yasumasa Nishimura
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka, Japan
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Cilla S, Deodato F, Romano C, Ianiro A, Macchia G, Re A, Buwenge M, Boldrini L, Indovina L, Valentini V, Morganti AG. Personalized automation of treatment planning in head-neck cancer: A step forward for quality in radiation therapy? Phys Med 2021; 82:7-16. [PMID: 33508633 DOI: 10.1016/j.ejmp.2020.12.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 12/04/2020] [Accepted: 12/19/2020] [Indexed: 01/09/2023] Open
Abstract
PURPOSE To perform a comprehensive dosimetric and clinical evaluation of the new Pinnacle Personalized automated planning system for complex head-and-neck treatments. METHODS Fifteen consecutive head-neck patients were enrolled. Radiotherapy was prescribed using VMAT with simultaneous integrated boost strategy. Personalized planning integrates the Feasibility engine able to supply an "a priori" DVH prediction of the achievability of planning goals. Comparison between clinically accepted manually-generated (MP) and automated (AP) plans was performed using dose-volume histograms and a blinded clinical evaluation by two radiation oncologists. Planning time between MP and AP was compared. Dose accuracy was validated using the PTW Octavius-4D phantom together with the 1500 2D-array. RESULTS For similar targets coverage, AP plans reported less irradiation of healthy tissue, with significant dose reduction for spinal cord, brainstem and parotids. On average, the mean dose to parotids and maximal doses to spinal cord and brainstem were reduced by 13-15% (p < 0.001), 9% (p < 0.001) and 16% (p < 0.001), respectively. The integral dose was reduced by 16% (p < 0.001). The dose conformity for the three PTVs was significantly higher with AP plans (p < 0.001). The two oncologists chose AP plans in more than 80% of cases. Overall planning times were reduced to <30 min for automated optimization. All AP plans passed the 3%/2 mm γ-analysis by more than 95%. CONCLUSION Complex head-neck plans created using Personalized automated engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues. The Feasibility module allowed OARs dose sparing well beyond the clinical objectives.
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Affiliation(s)
- Savino Cilla
- Medical Physics Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy.
| | - Francesco Deodato
- Radiation Oncology Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Carmela Romano
- Medical Physics Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Anna Ianiro
- Medical Physics Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Gabriella Macchia
- Radiation Oncology Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Alessia Re
- Radiation Oncology Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Milly Buwenge
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Italy; DIMES, Alma Mater Studiorum Bologna University, Italy
| | - Luca Boldrini
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli - Università Cattolica del Sacro Cuore, Roma, Italy
| | - Luca Indovina
- Medical Physics Unit, Fondazione Policlinico Universitario A. Gemelli - Università Cattolica del Sacro Cuore, Roma, Italy
| | - Vincenzo Valentini
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli - Università Cattolica del Sacro Cuore, Roma, Italy
| | - Alessio G Morganti
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Italy; DIMES, Alma Mater Studiorum Bologna University, Italy
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21
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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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22
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Rago M, Placidi L, Polsoni M, Rambaldi G, Cusumano D, Greco F, Indovina L, Menna S, Placidi E, Stimato G, Teodoli S, Mattiucci GC, Chiesa S, Marazzi F, Masiello V, Valentini V, De Spirito M, Azario L. Evaluation of a generalized knowledge-based planning performance for VMAT irradiation of breast and locoregional lymph nodes-Internal mammary and/or supraclavicular regions. PLoS One 2021; 16:e0245305. [PMID: 33449952 PMCID: PMC7810311 DOI: 10.1371/journal.pone.0245305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 12/24/2020] [Indexed: 11/29/2022] Open
Abstract
PURPOSE To evaluate the performance of eleven Knowledge-Based (KB) models for planning optimization (RapidPlantm (RP), Varian) of Volumetric Modulated Arc Therapy (VMAT) applied to whole breast comprehensive of nodal stations, internal mammary and/or supraclavicular regions. METHODS AND MATERIALS Six RP models have been generated and trained based on 120 VMAT plans data set with different criteria. Two extra-structures were delineated: a PTV for the optimization and a ring structure. Five more models, twins of the previous models, have been created without the need of these structures. RESULTS All models were successfully validated on an independent cohort of 40 patients, 30 from the same institute that provided the training patients and 10 from an additional institute, with the resulting plans being of equal or better quality compared with the clinical plans. The internal validation shows that the models reduce the heart maximum dose of about 2 Gy, the mean dose of about 1 Gy and the V20Gy of 1.5 Gy on average. Model R and L together with model B without optimization structures ensured the best outcomes in the 20% of the values compared to other models. The external validation observed an average improvement of at least 16% for the V5Gy of lungs in RP plans. The mean heart dose and for the V20Gy for lung IPSI were almost halved. The models reduce the maximum dose for the spinal canal of more than 2 Gy on average. CONCLUSIONS All KB models allow a homogeneous plan quality and some dosimetric gains, as we saw in both internal and external validation. Sub-KB models, developed by splitting right and left breast cases or including only whole breast with locoregional lymph nodes, have shown good performances, comparable but slightly worse than the general model. Finally, models generated without the optimization structures, performed better than the original ones.
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Affiliation(s)
- Maria Rago
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Lorenzo Placidi
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Mattia Polsoni
- Fatebenefratelli Isola Tiberina, Ospedale San Giovanni Calibita, Rome, Italy
- Amethyst Radioterapia Italia, Isola Tiberina, Rome, Italy
| | - Giulia Rambaldi
- Fatebenefratelli Isola Tiberina, Ospedale San Giovanni Calibita, Rome, Italy
- Amethyst Radioterapia Italia, Isola Tiberina, Rome, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Francesca Greco
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Luca Indovina
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Sebastiano Menna
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Elisa Placidi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Stefania Teodoli
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Silvia Chiesa
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Fabio Marazzi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Valeria Masiello
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Vincenzo Valentini
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Marco De Spirito
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Luigi Azario
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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Koike Y, Anetai Y, Takegawa H, Ohira S, Nakamura S, Tanigawa N. Deep learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning. Phys Med 2020; 78:8-14. [DOI: 10.1016/j.ejmp.2020.08.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 08/17/2020] [Accepted: 08/18/2020] [Indexed: 01/27/2023] Open
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Monzen H, Tamura M, Ueda Y, Fukunaga JI, Kamima T, Muraki Y, Kubo K, Nakamatsu K. Dosimetric evaluation with knowledge-based planning created at different periods in volumetric-modulated arc therapy for prostate cancer: a multi-institution study. Radiol Phys Technol 2020; 13:327-335. [PMID: 32986184 DOI: 10.1007/s12194-020-00585-0] [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: 03/31/2020] [Revised: 09/16/2020] [Accepted: 09/16/2020] [Indexed: 12/22/2022]
Abstract
Dosimetric evaluation and variation assessment were performed with two knowledge-based planning (KBP) models created at different periods for volumetric-modulated arc therapy (VMAT) for prostate cancer at five institutes. The first and second models (F- and S-models) for KBP were created before April 2017 and April 2019, respectively. The S-model was created using feedback plans from the F-model. Dose evaluation was compared between the two models using the same two computed tomography (CT) datasets and structures. The evaluation metrics were the dose received by 95.0% and 2.0% of the planning target volume (PTV); dose-volume parameters to the rectum and bladder as V90, V80, and V50; and monitor unit (MU). Dosimetric variation was compared by exporting estimated dose-volume histograms for each model to the Model Analytics website and assessing the organ at risk volume. There were no dosimetric differences between the two models for PTV. The V50 of the rectum in the S-model had improved compared to that of the F-model (case I: 49.3 ± 15.6 and 43.5 ± 15.2 [p = 0.08]; case II: 42.5 ± 16.9 and 36.0 ± 15.6 [p = 0.138]). The differences in other parameters were within ± 1.8% between the rectum and the bladder. The MU was slightly higher in the S-model than in the F-model, and dosimetric variation was reduced to the rectum and bladder among all the institutes. The polished S-model for KBP could be used for standardization of the plan quality and sharing of KBP models in VMAT for prostate cancer.
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Affiliation(s)
- Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan.
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 537-8567, Japan
| | - Jun-Ichi Fukunaga
- Divisin of Radiology, Department of Medical Technology, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Tatsuya Kamima
- Department of Radiation Oncology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan
| | - Yuta Muraki
- Department of Radiology, Seirei Hamamatsu General Hospital, 2-12-12 Sumiyoshi, Naka-ku, Hamamatsu, Shizuoka, 430-8558, Japan
| | - Kazuki Kubo
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan
| | - Kiyoshi Nakamatsu
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan
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25
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Ueda Y, Monzen H, Fukunaga JI, Ohira S, Tamura M, Suzuki O, Inui S, Isono M, Miyazaki M, Sumida I, Ogawa K, Teshima T. Characterization of knowledge-based volumetric modulated arc therapy plans created by three different institutions' models for prostate cancer. Rep Pract Oncol Radiother 2020; 25:1023-1028. [PMID: 33390859 DOI: 10.1016/j.rpor.2020.08.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 06/26/2020] [Accepted: 08/14/2020] [Indexed: 11/17/2022] Open
Abstract
Background The aim of this study was to clarify factors predicting the performance of knowledge-based planning (KBP) models in volume modulated arc therapy for prostate cancer in terms of sparing the organ at risk (OAR). Materials and methods In three institutions, each KBP model was trained by more than 20 library plans (LP) per model. To validate the characterization of each KBP model, 45 validation plans (VP) were calculated by the KBP system. The ratios of overlap between the OAR volume and the planning target volume (PTV) to the whole organ volume (Voverlap/Vwhole) were analyzed for each LP and VP. Regression lines between dose-volume parameters (V90, V75, and V50) and Voverlap/Vwhole were evaluated. The mean OAR dose, V90, V75, and V50 of LP did not necessarily match those of VP. Results In both the rectum and bladder, the dose-volume parameters for VP were strongly correlated with Voverlap/Vwhole at institutes A, B, and C (R > 0.74, 0.85, and 0.56, respectively). Except in the rectum at institute B, the slopes of the regression lines for LP corresponded to those for VP. For dose-volume parameters for the rectum, the ratios of slopes of the regression lines in VP to those in LP ranged 0.51-1.26. In the bladder, most ratios were less than 1.0 (mean: 0.77). Conclusion For each OAR, each model made distinct dosimetric characterizations in terms of Voverlap/Vwhole. The relationship between dose-volume parameters and Voverlap/Vwhole of OARs in LP predicts the KBP models' performance sparing OARs.
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Affiliation(s)
- Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 537-8567, Japan
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka 565-0071, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Onohigashi, Osakasayama, Osaka 589-8511, Japan
| | - Jun-Ichi Fukunaga
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Shingo Ohira
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 537-8567, Japan
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Onohigashi, Osakasayama, Osaka 589-8511, Japan
| | - Osamu Suzuki
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka 565-0071, Japan
| | - Shoki Inui
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 537-8567, Japan
| | - Masaru Isono
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 537-8567, Japan
| | - Masayoshi Miyazaki
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 537-8567, Japan
| | - Iori Sumida
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka 565-0071, Japan
| | - Kazuhiko Ogawa
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka 565-0071, Japan
| | - Teruki Teshima
- Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 537-8567, Japan
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Tamura M, Monzen H, Matsumoto K, Kubo K, Ueda Y, Kamima T, Inada M, Doi H, Nakamatsu K, Nishimura Y. Influence of Cleaned-up Commercial Knowledge-Based Treatment Planning on Volumetric-Modulated Arc Therapy of Prostate Cancer. J Med Phys 2020; 45:71-77. [PMID: 32831489 PMCID: PMC7416859 DOI: 10.4103/jmp.jmp_109_19] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 04/02/2020] [Accepted: 04/21/2020] [Indexed: 01/23/2023] Open
Abstract
Purpose: This study aimed to investigate the influence of cleaned-up knowledge-based treatment planning (KBP) models on the plan quality for volumetric-modulated arc therapy (VMAT) of prostate cancer. Materials and Methods: Thirty prostate cancer VMAT plans were enrolled and evaluated according to four KBP modeling methods as follows: (1) model not cleaned – trained by fifty other clinical plans (KBPORIG); (2) cases cleaned by removing plans that did not meet all clinical goals of the dosimetric parameters, derived from dose–volume histogram (DVH) (KBPC-DVH); (3) cases cleaned outside the range of ±1 standard deviation through the principal component analysis regression plots (KBPC-REG); and (4) cases cleaned using both methods (2) and (3) (KBPC-ALL). Rectal and bladder structures in the training models numbered 34 and 48 for KBPC-DVH, 37 and 33 for KBPC-REG, and 26 and 33 for KBPC-ALL, respectively. The dosimetric parameters for each model with one-time auto-optimization were compared. Results: All KBP models improved target dose coverage and conformity and provided comparable sparing of organs at risks (rectal and bladder walls). There were no significant differences in plan quality among the KBP models. Nevertheless, only the KBPC-ALL model generated no cases of >1% V78 Gy (prescribed dose) to the rectal wall, whereas the KBPORIG, KBPC-DVH, and KBPC-REG models included two, four, and three cases, respectively, which were difficult to overcome with KBP because the planning target volume (PTV) and rectum regions overlapped. Conclusions: The cleaned-up KBP model based on DVH and regression plots improved plan quality in the PTV–rectum overlap region.
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Affiliation(s)
- Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osaka, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osaka, Japan
| | - Kenji Matsumoto
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osaka, Japan
| | - Kazuki Kubo
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osaka, Japan
| | - Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Tatsuya Kamima
- Department of Radiation Oncology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Koto, Tokyo, Japan
| | - Masahiro Inada
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Hiroshi Doi
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Kiyoshi Nakamatsu
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
| | - Yasumasa Nishimura
- Department of Radiation Oncology, Faculty of Medicine, Kindai University, Osaka, Japan
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Uehara T, Monzen H, Tamura M, Ishikawa K, Doi H, Nishimura Y. Dose-volume histogram analysis and clinical evaluation of knowledge-based plans with manual objective constraints for pharyngeal cancer. JOURNAL OF RADIATION RESEARCH 2020; 61:499-505. [PMID: 32329509 PMCID: PMC7299264 DOI: 10.1093/jrr/rraa021] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 11/01/2019] [Indexed: 06/11/2023]
Abstract
The present study aimed to evaluate whether knowledge-based plans (KBP) from a single optimization could be used clinically, and to compare dose-volume histogram (DVH) parameters and plan quality between KBP with (KBPCONST) and without (KBPORIG) manual objective constraints and clinical manual optimized (CMO) plans for pharyngeal cancer. KBPs were produced from a system trained on clinical plans from 55 patients with pharyngeal cancer who had undergone intensity-modulated radiation therapy or volumetric-modulated arc therapy (VMAT). For another 15 patients, DVH parameters of KBPCONST and KBPORIG from a single optimization were compared with CMO plans with respect to the planning target volume (D98%, D50%, D2%), brainstem maximum dose (Dmax), spinal cord Dmax, parotid gland median and mean dose (Dmed and Dmean), monitor units and modulation complexity score for VMAT. The Dmax of spinal cord and brainstem and the Dmed and Dmean of ipsilateral parotid glands were unacceptably high for KBPORIG, although the KBPCONST DVH parameters met our goal for most patients. KBPCONST and CMO plans produced comparable DVH parameters. The monitor units of KBPCONST were significantly lower than those of the CMO plans (P < 0.001). Dose distribution of the KBPCONST was better than or comparable to that of the CMO plans for 13 (87%) of the 15 patients. In conclusion, KBPORIG was found to be clinically unacceptable, while KBPCONST from a single optimization was comparable or superior to CMO plans for most patients with head and neck cancer.
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Affiliation(s)
- Takuya Uehara
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka-Sayama, Osaka, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osaka-Sayama, Osaka, Japan
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osaka-Sayama, Osaka, Japan
| | - Kazuki Ishikawa
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka-Sayama, Osaka, Japan
| | - Hiroshi Doi
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka-Sayama, Osaka, Japan
| | - Yasumasa Nishimura
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osaka-Sayama, Osaka, Japan
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Murakami Y, Magome T, Matsumoto K, Sato T, Yoshioka Y, Oguchi M. Fully automated dose prediction using generative adversarial networks in prostate cancer patients. PLoS One 2020; 15:e0232697. [PMID: 32365088 PMCID: PMC7197852 DOI: 10.1371/journal.pone.0232697] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 04/19/2020] [Indexed: 12/20/2022] Open
Abstract
Purpose Although dose prediction for intensity modulated radiation therapy (IMRT) has been accomplished by a deep learning approach, delineation of some structures is needed for the prediction. We sought to develop a fully automated dose-generation framework for IMRT of prostate cancer by entering the patient CT datasets without the contour information into a generative adversarial network (GAN) and to compare its prediction performance to a conventional prediction model trained from patient contours. Methods We propose a synthetic approach to translate patient CT datasets into a dose distribution for IMRT. The framework requires only paired-images, i.e., patient CT images and corresponding RT-doses. The model was trained from 81 IMRT plans of prostate cancer patients, and then produced the dose distribution for 9 test cases. To compare its prediction performance to that of another trained model, we created a model trained from structure images. Dosimetric parameters for the planning target volume (PTV) and organs at risk (OARs) were calculated from the generated and original dose distributions, and mean differences of dosimetric parameters were compared between the CT-based model and the structure-based model. Results The mean differences of all dosimetric parameters except for D98% and D95% for PTV were within approximately 2% and 3% of the prescription dose for OARs in the CT-based model, while the differences in the structure-based model were within approximately 1% for PTV and approximately 2% for OARs, with a mean prediction time of 5 seconds per patient. Conclusions Accurate and rapid dose prediction was achieved by the learning of patient CT datasets by a GAN-based framework. The CT-based dose prediction could reduce the time required for both the iterative optimization process and the structure contouring, allowing physicians and dosimetrists to focus their expertise on more challenging cases.
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Affiliation(s)
- Yu Murakami
- Graduate Division of Health Sciences, Komazawa University, Komazawa, Setagaya-ku, Tokyo, Japan
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Ariake, Koto-ku, Tokyo, Japan
| | - Taiki Magome
- Graduate Division of Health Sciences, Komazawa University, Komazawa, Setagaya-ku, Tokyo, Japan
- * E-mail:
| | - Kazuki Matsumoto
- Graduate Division of Health Sciences, Komazawa University, Komazawa, Setagaya-ku, Tokyo, Japan
| | - Tomoharu Sato
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Ariake, Koto-ku, Tokyo, Japan
| | - Yasuo Yoshioka
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Ariake, Koto-ku, Tokyo, Japan
| | - Masahiko Oguchi
- Radiation Oncology Department, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Ariake, Koto-ku, Tokyo, Japan
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Ueda Y, Miyazaki M, Sumida I, Ohira S, Tamura M, Monzen H, Tsuru H, Inui S, Isono M, Ogawa K, Teshima T. Knowledge-based planning for oesophageal cancers using a model trained with plans from a different treatment planning system. Acta Oncol 2020; 59:274-283. [PMID: 31755332 DOI: 10.1080/0284186x.2019.1691257] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Background: This study aimed to evaluate knowledge-based volume modulated arc therapy (VMAT) plans for oesophageal cancers using a model trained with plans optimised with a different treatment planning system (TPS) and to compare lung dose sparing in two TPSs, Eclipse and RayStation.Materials and methods: A total of 64 patients with stage I-III oesophageal cancers were treated using hybrid VMAT (H-VMAT) plans optimised using RayStation. Among them, 40 plans were used for training the model for knowledge-based planning (KBP) in RapidPlan. The remaining 24 plans were recalculated using RapidPlan to validate the KBP model. H-VMAT plans calculated using RapidPlan were compared with H-VMAT plans optimised using RayStation with respect to planning target volume doses, lung doses, and modulation complexity.Results: In the lung, there were significant differences between the volume ratios receiving doses in excess of 5, 10, and 20 Gy (V5, V10, and V20). The V5 for the lung with H-VMAT plans optimised using RapidPlan was significantly higher than that of H-VMAT plans optimised using RayStation (p < .01), with a mean difference of 10%. Compared to H-VMAT plans optimised using RayStation, the V10 and V20 for the lung were significantly lower with H-VMAT plans optimised using RapidPlan (p = .04 and p = .02), with differences exceeding 1.0%. In terms of modulation complexity, the change in beam output at each control point was more constant with H-VMAT plans optimised using RapidPlan than with H-VMAT plans optimised using RayStation. The range of the change with H-VMAT plans optimised using RapidPlan was one third that of H-VMAT plans optimised using RayStation.Conclusion: Two optimisers in Eclipse and RayStation had different dosimetric performance in lung sparing and modulation complexity. RapidPlan could not improve low lung doses, however, it provided an appreciate intermediated doses compared to plans optimised with RayStation.
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Affiliation(s)
- Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Masayoshi Miyazaki
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Iori Sumida
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Shingo Ohira
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osaka, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, Osaka, Japan
| | - Haruhi Tsuru
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Shoki Inui
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Masaru Isono
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Kazuhiko Ogawa
- Department of Radiation Oncology, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Teruki Teshima
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
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Kubo K, Monzen H, Ishii K, Tamura M, Nakasaka Y, Kusawake M, Kishimoto S, Nakahara R, Matsuda S, Nakajima T, Kawamorita R. Inter-planner variation in treatment-plan quality of plans created with a knowledge-based treatment planning system. Phys Med 2019; 67:132-140. [PMID: 31706149 DOI: 10.1016/j.ejmp.2019.10.032] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 10/15/2019] [Accepted: 10/17/2019] [Indexed: 10/25/2022] Open
Abstract
PURPOSE This study aimed to clarify the inter-planner variation of plan quality in knowledge-based plans created by nine planners. METHODS Five hypofractionated prostate-only (HPO) volumetric modulated arc therapy (VMAT) plans and five whole-pelvis (WP) VMAT plans were created by each planner using a knowledge-based planning (KBP) system. Nine planners were divided into three groups of three planners each: Senior, Junior, and Beginner. Single optimization with only priority modification for all objectives was performed to stay within the dose constraints. The coefficients of variation (CVs) for dosimetric parameters were evaluated, and a plan quality metric (PQM) was used to evaluate comprehensive plan quality. RESULTS Lower CVs (<0.05) were observed at dosimetric parameters in the planning target volume for both HPO and WP plans, while the CVs in the rectum and bladder for WP plans (<0.91) were greater than those for HPO plans (<0.17). The PQM values of HPO plans for Cases1-5 (average ± standard deviation) were 41.2 ± 7.1, 40.9 ± 5.6, and 39.9 ± 4.6 in the Senior, Junior, and Beginner groups, respectively. For the WP plans, the PQM values were 51.9 ± 6.3, 47.5 ± 4.3, and 40.0 ± 6.6, respectively. The number of clinically acceptable HPO and WP plans were 13/15 and 11/15 in the Senior group, 13/15 and 10/15 plans in the Junior group, and 8/15 and 2/15 plans in the Beginner group, respectively. CONCLUSION Inter-planner variation in the plan quality with RapidPlan remains, especially for the complicated VMAT plans, due to planners' heuristics.
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Affiliation(s)
- Kazuki Kubo
- Department of Radiation Oncology, Tane General Hospital, 1-12-21 Kujo-minami, Nishi, Osaka 550-0025, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Ohno-higashi, Osaka-sayama, Osaka 589-8511, Japan.
| | - Kentaro Ishii
- Department of Radiation Oncology, Tane General Hospital, 1-12-21 Kujo-minami, Nishi, Osaka 550-0025, Japan
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Sciences, Kindai University, 377-2 Ohno-higashi, Osaka-sayama, Osaka 589-8511, Japan
| | - Yuta Nakasaka
- Department of Radiation Oncology, Tane General Hospital, 1-12-21 Kujo-minami, Nishi, Osaka 550-0025, Japan
| | - Masayuki Kusawake
- Department of Radiation Oncology, Tane General Hospital, 1-12-21 Kujo-minami, Nishi, Osaka 550-0025, Japan
| | - Shun Kishimoto
- Department of Radiation Oncology, Tane General Hospital, 1-12-21 Kujo-minami, Nishi, Osaka 550-0025, Japan
| | - Ryuta Nakahara
- Department of Radiation Oncology, Tane General Hospital, 1-12-21 Kujo-minami, Nishi, Osaka 550-0025, Japan
| | - Shogo Matsuda
- Department of Radiation Oncology, Tane General Hospital, 1-12-21 Kujo-minami, Nishi, Osaka 550-0025, Japan
| | - Toshifumi Nakajima
- Department of Radiation Oncology, Tane General Hospital, 1-12-21 Kujo-minami, Nishi, Osaka 550-0025, Japan
| | - Ryu Kawamorita
- Department of Radiation Oncology, Tane General Hospital, 1-12-21 Kujo-minami, Nishi, Osaka 550-0025, Japan
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