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Roberfroid B, Chocan Vera MS, Draguet C, Lee JA, Barragán-Montero AM, Sterpin E. Anticipating potential bottlenecks in adaptive proton FLASH therapy: a ridge filter reuse strategy. Phys Med Biol 2025; 70:065005. [PMID: 39993377 DOI: 10.1088/1361-6560/adb9b2] [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: 11/29/2024] [Accepted: 02/24/2025] [Indexed: 02/26/2025]
Abstract
Objective.Achieving FLASH dose rate with pencil beam scanning intensity modulated proton therapy is challenging. However, utilizing a single energy layer with a ridge filter (RF) can maintain dose rate and conformality. Yet, changes in patient anatomy over the treatment course can render the RF obsolete. Unfortunately, creating a new RF is time-consuming, thus, incompatible with online adaptation. To address this, we propose to re-optimize the spot weights while keeping the same initial RF.Approach.Data from six head and neck cancer patients with a repeated computed tomography (CT2) were used. FLASH treatment plans were generated with three methods on CT2: 'full-adaptation' (FA), optimized from scratch with a new RF; 'spot-adaptation only' (SAO), re-using initial RF but adjusting plan spot weights; and 'no adaptation' (NoA) where the dose from initial plans on initial CT (CT1) was recomputed on CT2. The prescribed dose per fraction was 9 Gy. Different beam angles were tested for each CT2(1 beam per fraction). The FA, SAO and NoA plans were then compared on CT2.Main results.Fractions with SAO showed a median decrease of 0.05 Gy forD98% and a median increase of 0.03 Gy forD2% of CTV when compared to their homologous FA plans on nominal case. Median conformity number decreased by 0.03. Median max dose to spinal cord increased by 0.09 Gy. The largest median increase in mean dose to organs was 0.03 Gy to the mandible. The largest observed median difference in organs receiving a minimal dose rate of 40 Gy s-1was 0.5% for the mandible. Up to 16 of the 20 evaluated SAO fractions were thus deemed clinically acceptable, with up to 8 NoA plans already acceptable before adaptation.Significance.Proposed SAO workflow showed that for most of our evaluated plans, daily reprinting of RF was not necessary.
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Affiliation(s)
- Benjamin Roberfroid
- Université catholique de Louvain-Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
| | - Macarena S Chocan Vera
- Université catholique de Louvain-Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
| | - Camille Draguet
- Université catholique de Louvain-Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
| | - John A Lee
- Université catholique de Louvain-Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
| | - Ana M Barragán-Montero
- Université catholique de Louvain-Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
| | - Edmond Sterpin
- Université catholique de Louvain-Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
- KU Leuven-Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium
- Particle Therapy Interuniversity Center Leuven-PARTICLE, Leuven, Belgium
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van Genderingen J, Nguyen D, Knuth F, Nomer HAA, Incrocci L, Sharfo AWM, Zolnay A, Oelfke U, Jiang S, Rossi L, Heijmen BJM, Breedveld S. Deep learning dose prediction to approach Erasmus-iCycle dosimetric plan quality within seconds for instantaneous treatment planning. Radiother Oncol 2025; 203:110662. [PMID: 39647528 DOI: 10.1016/j.radonc.2024.110662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 09/24/2024] [Accepted: 11/19/2024] [Indexed: 12/10/2024]
Abstract
BACKGROUND AND PURPOSE Fast, high-quality deep learning (DL) prediction of patient-specific 3D dose distributions can enable instantaneous treatment planning (IP), in which the treating physician can evaluate the dose and approve the plan immediately after contouring, rather than days later. This would greatly benefit clinical workload, patient waiting times and treatment quality. IP requires that predicted dose distributions closely match the ground truth. This study examines how training dataset size and model size affect dose prediction accuracy for Erasmus-iCycle GT plans to enable IP. MATERIALS AND METHODS For 1250 prostate patients, dose distributions were automatically generated using Erasmus-iCycle. Hierarchically Densely Connected U-Nets with 2/3/4/5/6 pooling layers were trained with datasets of 50/100/250/500/1000 patients, using a validation set of 100 patients. A fixed test set of 150 patients was used for evaluations. RESULTS For all model sizes, prediction accuracy increased with the number of training patients, without levelling off at 1000 patients. For 4-6 level models with 1000 training patients, prediction accuracies were high and comparable. For 6 levels and 1000 training patients, the median prediction errors and interquartile ranges for PTV V95%, rectum V75Gy and bladder V65Gy were 0.01 [-0.06,0.15], 0.01 [-0.20,0.29] and -0.02 [-0.27,0.27] %-point. Dose prediction times were around 1.2 s. CONCLUSION Although even for 1000 training patients there was no convergence in obtained prediction accuracy yet, the accuracy for the 6-level model with 1000 training patients may be adequate for the pursued instantaneous planning, which is subject of further research.
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Affiliation(s)
- Joep van Genderingen
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands.
| | - Dan Nguyen
- UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, Dallas, USA
| | - Franziska Knuth
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands
| | - Hazem A A Nomer
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands
| | - Luca Incrocci
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands
| | - Abdul Wahab M Sharfo
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands
| | - András Zolnay
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands
| | - Uwe Oelfke
- The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Joint Department of Physics, London, United Kingdom
| | - Steve Jiang
- UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, Dallas, USA
| | - Linda Rossi
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands
| | - Ben J M Heijmen
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands
| | - Sebastiaan Breedveld
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands
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Roberfroid B, Huet-Dastarac M, Borderías-Villarroel E, Koffeing R, Lee JA, Barragán-Montero AM, Sterpin E. Towards faster plan adaptation for proton arc therapy using initial treatment plan information. Phys Imaging Radiat Oncol 2025; 33:100705. [PMID: 40008280 PMCID: PMC11851183 DOI: 10.1016/j.phro.2025.100705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2024] [Revised: 01/14/2025] [Accepted: 01/21/2025] [Indexed: 02/27/2025] Open
Abstract
Background and Purpose Proton arc therapy (PAT) is an emerging modality delivering continuously rotating proton beams. Current PAT planning approaches are time-consuming, making them unsuitable for online adaptation. This study proposes an accelerated workflow for adapting PAT plans. Materials and Methods The proposed workflow transfers spots from initial computed tomography (CT) to the CT of the day, updates energy layers considering the initial pattern, and re-optimizes selected transferred spots based on their initial weights and impact on the objective function.A retrospective study was conducted on five head and neck patients who underwent plan adaptation on a repeated CT. PAT plans were generated with two different methods on the repeated CT: reference, created de novo, and smart-adapted, generated with the proposed adaptive workflow. Robust optimization was performed for all plans. Results Smart-adapted plans achieved similar mean dose to organs at risk as the reference: the largest median increase of mean dose was 1.9 Gy to the mandible; the median of maximum dose to spinal cord was 0.5 Gy lower for the smart-adapted plans. The median target coverage, i.e. D98, to primary tumor and nodes of smart-adapted plans decreased by 0.2 and 0.4 Gy for the nominal case, and 0.4 and 0.6 Gy for the worst-case scenario; all smart-adapted plans met clinical objectives. The smart-adaptation method reduced average planning time from 19184 s to 5626 s, a 3.4-fold improvement. Conclusions Smart-adapted plans achieve similar plan quality to the reference method, while significantly reducing plan generation time for new patient anatomy.
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Affiliation(s)
- Benjamin Roberfroid
- Université catholique de Louvain – Center of Molecular Imaging, Radiotherapy and Oncology (MIRO) Brussels Belgium
| | - Margerie Huet-Dastarac
- Université catholique de Louvain – Center of Molecular Imaging, Radiotherapy and Oncology (MIRO) Brussels Belgium
| | - Elena Borderías-Villarroel
- Université catholique de Louvain – Center of Molecular Imaging, Radiotherapy and Oncology (MIRO) Brussels Belgium
| | - Rodin Koffeing
- Université catholique de Louvain – Center of Molecular Imaging, Radiotherapy and Oncology (MIRO) Brussels Belgium
| | - John A. Lee
- Université catholique de Louvain – Center of Molecular Imaging, Radiotherapy and Oncology (MIRO) Brussels Belgium
| | - Ana M. Barragán-Montero
- Université catholique de Louvain – Center of Molecular Imaging, Radiotherapy and Oncology (MIRO) Brussels Belgium
| | - Edmond Sterpin
- Université catholique de Louvain – Center of Molecular Imaging, Radiotherapy and Oncology (MIRO) Brussels Belgium
- KU Leuven – Department of Oncology Laboratory of Experimental Radiotherapy Leuven Belgium
- Particle Therapy Interuniversity Center Leuven – PARTICLE Leuven Belgium
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Conroy L, Winter J, Khalifa A, Tsui G, Berlin A, Purdie TG. Artificial Intelligence for Radiation Treatment Planning: Bridging Gaps From Retrospective Promise to Clinical Reality. Clin Oncol (R Coll Radiol) 2025; 37:103630. [PMID: 39531894 DOI: 10.1016/j.clon.2024.08.005] [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: 12/06/2023] [Revised: 07/31/2024] [Accepted: 08/08/2024] [Indexed: 11/16/2024]
Abstract
Artificial intelligence (AI) radiation therapy (RT) planning holds promise for enhancing the consistency and efficiency of the RT planning process. Despite technical advancements, the widespread integration of AI into RT treatment planning faces challenges. The transition from controlled retrospective environments to real-world clinical settings introduces heightened scrutiny from clinical end users, potentially leading to decreased clinical acceptance. Key considerations for implementing AI RT planning include ensuring the AI model performance aligns with clinical standards, using high-quality training data, and incorporating sufficient data variation through meticulous curation by clinical experts. Beyond technical aspects, factors such as potential biases and the level of trust clinical end users place in AI may present unforeseen obstacles for real-world clinical use. Addressing these challenges requires bridging education and expertise gaps among clinical end users, enabling them to confidently embrace and utilize AI for routine RT planning. By fostering a better understanding of AI capabilities, building trust, and providing comprehensive training, the promises of AI RT planning can be a reality in the clinical setting. This article assesses the current clinical use of AI RT planning and explores challenges and considerations for bridging gaps in knowledge and expertise for AI operationalization, with focus on training data curation, workflow integration, explainability, bias, and domain knowledge. Remaining challenges in clinical implementation of AI RT treatment planning are examined in the context of trust building approaches.
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Affiliation(s)
- L Conroy
- Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada; Techna Insitute, University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada; Department of Radiation Oncology, University of Toronto, 149 College Street - Stewart Building Suite 504, Toronto, Ontario, M5T 1P5, Canada.
| | - J Winter
- Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada; Techna Insitute, University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada; Department of Radiation Oncology, University of Toronto, 149 College Street - Stewart Building Suite 504, Toronto, Ontario, M5T 1P5, Canada.
| | - A Khalifa
- Techna Insitute, University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada; Department of Medical Biophysics, University of Toronto, Princess Maragret Cancer Research Tower, MaRS Centre, 101 College Street, Room 15-701, Toronto, Ontario, M5G 1L7, Canada.
| | - G Tsui
- Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada.
| | - A Berlin
- Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada; Techna Insitute, University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada; Department of Radiation Oncology, University of Toronto, 149 College Street - Stewart Building Suite 504, Toronto, Ontario, M5T 1P5, Canada.
| | - T G Purdie
- Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada; Techna Insitute, University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada; Department of Radiation Oncology, University of Toronto, 149 College Street - Stewart Building Suite 504, Toronto, Ontario, M5T 1P5, Canada; Department of Medical Biophysics, University of Toronto, Princess Maragret Cancer Research Tower, MaRS Centre, 101 College Street, Room 15-701, Toronto, Ontario, M5G 1L7, Canada.
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Yoganathan SA, Basith A, Rostami A, Usman M, Paloor S, Hammoud R, Al-Hammadi N. Investigating the impact of rapidplan on ethos automated planning. Med Dosim 2024; 50:8-12. [PMID: 39079802 DOI: 10.1016/j.meddos.2024.06.003] [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: 04/30/2024] [Accepted: 06/10/2024] [Indexed: 02/03/2025]
Abstract
Automated planning has surged in popularity within external beam radiation therapy in recent times. Leveraging insights from previous clinical knowledge could enhance auto-planning quality. In this work, we evaluated the performance of Ethos automated planning with knowledge-based guidance, specifically using Rapidplan (RP). Seventy-four patients with head-and-neck (HN) cancer and 37 patients with prostate cancer were used to construct separate RP models. Additionally, 16 patients from each group (HN and prostate) were selected to assess the performance of Ethos auto-planning results. Initially, a template-based Ethos plan (Non-RP plan) was generated, followed by integrating the corresponding RP model's DVH estimates into the optimization process to generate another plan (RP plan). We compared the target coverage, OAR doses, and total monitor units between the non-RP and RP plans. Both RP and non-RP plans achieved comparable target coverage in HN and Prostate cases, with a negligible difference of less than 0.5% (p > 0.2). RP plans consistently demonstrated lower doses of OARs in both HN and prostate cases. Specifically, the mean doses of OARs were significantly reduced by 9% (p < 0.05). RP plans required slightly higher monitor units in both HN and prostate sites (p < 0.05), however, the plan generation time was almost similar (p > 0.07). The inclusion of the RP model reduced the OAR doses, particularly reducing the mean dose to critical organs compared to non-RP plans while maintaining similar target coverage. Our findings provide valuable insights for clinics adopting Ethos planning, potentially enhancing the auto-planning to operate optimally.
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Affiliation(s)
- S A Yoganathan
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
| | - Ahamed Basith
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Aram Rostami
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Muhammad Usman
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Satheesh Paloor
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Rabih Hammoud
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Noora Al-Hammadi
- Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
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Ersted Rasmussen M, Dueholm Vestergaard C, Folsted Kallehauge J, Ren J, Haislund Guldberg M, Nørrevang O, Vindelev Elstrøm U, Sofia Korreman S. RadDeploy: A framework for integrating in-house developed software and artificial intelligence models seamlessly into radiotherapy workflows. Phys Imaging Radiat Oncol 2024; 31:100607. [PMID: 39071159 PMCID: PMC11283118 DOI: 10.1016/j.phro.2024.100607] [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: 04/29/2024] [Revised: 06/20/2024] [Accepted: 06/27/2024] [Indexed: 07/30/2024] Open
Abstract
The use of and research in automation and artificial intelligence (AI) in radiotherapy is moving with incredible pace. Many innovations do, however, not make it into the clinic. One technical reason for this may be the lack of a platform to deploy such software into clinical practice. We suggest RadDeploy as a framework for integrating containerized software in clinical workflows outside of treatment planning systems. RadDeploy supports multiple DICOM as input for model containers and can run model containers asynchronously across GPUs and computers. This technical note summarizes the inner workings of RadDeploy and demonstrates three use-cases with varying complexity.
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Affiliation(s)
- Mathis Ersted Rasmussen
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Oncology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, 8200 Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark
| | - Casper Dueholm Vestergaard
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark
| | - Jesper Folsted Kallehauge
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
| | - Jintao Ren
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark
| | - Maiken Haislund Guldberg
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
| | - Ole Nørrevang
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
| | - Ulrik Vindelev Elstrøm
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
| | - Stine Sofia Korreman
- Danish Centre for Particle Therapy, Aarhus University Hospital, Palle Juul-Jensens Boulevard 25, 8200 Aarhus N, Denmark
- Department of Oncology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, 8200 Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark
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Prunaretty J, Lopez L, Cabaillé M, Bourgier C, Morel A, Azria D, Fenoglietto P. Evaluation of Ethos intelligent optimization engine for left locally advanced breast cancer. Front Oncol 2024; 14:1399978. [PMID: 39015493 PMCID: PMC11250590 DOI: 10.3389/fonc.2024.1399978] [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: 03/12/2024] [Accepted: 06/13/2024] [Indexed: 07/18/2024] Open
Abstract
Purpose To evaluate the feasibility to use a standard Ethos planning template to treat left-sided breast cancer with regional lymph nodes. Material/Methods The tuning cohort of 5 patients was used to create a planning template. The validation cohort included 15 patients treated for a locally advanced left breast cancer randomly enrolled. The Ethos planning template was tuned using standard 3 partial arc VMAT and two collimator rotation configurations: 45/285/345° and 30/60/330°. Re-planning was performed automatically using the template without editing. The study was conducted with a schedule of 42.3 Gy in 18 fractions to the breast/chestwall, internal mammary chain (IMC) and regional lymph nodes ("Nodes"). The PTV was defined as a 3D extension of the CTV with a margin of 7 mm, excluding the 5mm below the skin. The manual treatment plans were performed using Eclipse treatment planning system with AAA and PO algorithms (v15.6) and a manual arc VMAT configuration and imported in Ethos TPS (v1.1) for a dose calculation with Ethos Acuros algorithm. The automated plans were compared with the manual plans using PTV and CTV coverage, homogeneity and conformity indices (HI and CN) and doses to organs at risk (OAR) via DVH metrics. For each plan, the patient quality assurance (QA) were performed using Mobius3D and gamma index. Finally, two breast radiation oncologists performed a blinded assessment of the clinical acceptability of each of the three plans (manual and automated) for each patient. Results The manual and automated plans provided suitable treatment planning as regards dose constraints. The dosimetric comparison showed the CTV_breast D99% were significantly improved with both automated plans (p< 0,002) while PTV coverage was comparable. The doses to the organs at risk were equivalent for the three plans. Concerning treatment delivery, the Ethos-45° and Ethos-30° plans led to an increase in MUs compared to the manual plans, without affecting the beam on time. The average gamma index pass rates remained consistently above 98% regardless of the type of plan utilized. In the blinded evaluation, clinicians 1 and 2 assessed 13 out of 15 plans for Ethos 45° and 11 out of 15 plans for Ethos 30° as clinically acceptable. Conclusion Using a standard planning template for locally advanced breast cancer, the Ethos TPS provided automated plans that were clinically acceptable and comparable in quality to manually generated plans. Automated plans also dramatically reduce workflow and operator variability.
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Affiliation(s)
- Jessica Prunaretty
- Radiotherapy Department, Montpellier Regional Cancer Institute, Montpellier, France
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Chang C, Bohannon D, Tian Z, Wang Y, Mcdonald MW, Yu DS, Liu T, Zhou J, Yang X. A retrospective study on the investigation of potential dosimetric benefits of online adaptive proton therapy for head and neck cancer. J Appl Clin Med Phys 2024; 25:e14308. [PMID: 38368614 PMCID: PMC11087169 DOI: 10.1002/acm2.14308] [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: 05/03/2023] [Revised: 10/28/2023] [Accepted: 02/06/2024] [Indexed: 02/20/2024] Open
Abstract
PURPOSE Proton therapy is sensitive to anatomical changes, often occurring in head-and-neck (HN) cancer patients. Although multiple studies have proposed online adaptive proton therapy (APT), there is still a concern in the radiotherapy community about the necessity of online APT. We have performed a retrospective study to investigate the potential dosimetric benefits of online APT for HN patients relative to the current offline APT. METHODS Our retrospective study has a patient cohort of 10 cases. To mimic online APT, we re-evaluated the dose of the in-use treatment plan on patients' actual treatment anatomy captured by cone-beam CT (CBCT) for each fraction and performed a templated-based automatic replanning if needed, assuming that these were performed online before treatment delivery. Cumulative dose of the simulated online APT course was calculated and compared with that of the actual offline APT course and the designed plan dose of the initial treatment plan (referred to as nominal plan). The ProKnow scoring system was employed and adapted for our study to quantify the actual quality of both courses against our planning goals. RESULTS The average score of the nominal plans over the 10 cases is 41.0, while those of the actual offline APT course and our simulated online course is 25.8 and 37.5, respectively. Compared to the offline APT course, our online course improved dose quality for all cases, with the score improvement ranging from 0.4 to 26.9 and an average improvement of 11.7. CONCLUSION The results of our retrospective study have demonstrated that online APT can better address anatomical changes for HN cancer patients than the current offline replanning practice. The advanced artificial intelligence based automatic replanning technology presents a promising avenue for extending potential benefits of online APT.
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Affiliation(s)
- Chih‐Wei Chang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Duncan Bohannon
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Zhen Tian
- Department of Radiation and Cellular OncologyUniversity of ChicagoChicagoIllinoisUSA
| | - Yinan Wang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Mark W. Mcdonald
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - David S. Yu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Tian Liu
- Department of Radiation OncologyMount Sinai Medical CenterNew YorkNew YorkUSA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
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Roberfroid B, Lee JA, Geets X, Sterpin E, Barragán-Montero AM. DIVE-ART: A tool to guide clinicians towards dosimetrically informed volume editions of automatically segmented volumes in adaptive radiation therapy. Radiother Oncol 2024; 192:110108. [PMID: 38272315 DOI: 10.1016/j.radonc.2024.110108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 01/17/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Affiliation(s)
- Benjamin Roberfroid
- Université catholique de Louvain - Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium.
| | - John A Lee
- Université catholique de Louvain - Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
| | - Xavier Geets
- Université catholique de Louvain - Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium; Cliniques universitaires Saint-Luc, Department of Radiation Oncology, Brussels, Belgium
| | - Edmond Sterpin
- Université catholique de Louvain - Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium; KU Leuven - Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium; Particle Therapy Interuniversity Center Leuven - PARTICLE, Leuven, Belgium
| | - Ana M Barragán-Montero
- Université catholique de Louvain - Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
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