ArticleRadiation oncology (London, England)2014
Assessment of a model based optimization engine for volumetric modulated arc therapy for patients with advanced hepatocellular cancer.
Article in Radiation oncology (London, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
- Improving Quality and Consistency in NRG Oncology Radiation Therapy Oncology Group 0631 for Spine Radiosurgery via Knowledge-Based Planning.International journal of radiation oncology, biology, physics · 2018Trial
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- Criteria-calibration approaches to deep learning-based cervical cancer radiation treatment auto-planning.Radiation oncology (London, England) · 2025Article
- Systematic implementation of rapidplan for prostate cancer: toward a unified knowledge-based planning model.Scientific reports · 2025Article
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- Critical assessment of knowledge-based models for craniospinal irradiation of paediatric patients.Physics and imaging in radiation oncology · 2025Article
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- Effectiveness of multi-criteria optimization in combination with knowledge-based modeling in radiotherapy of left-sided breast including regional nodes.Physics and imaging in radiation oncology · 2024Article
- Effects of model size and composition on quality of head-and-neck knowledge-based plans.Journal of applied clinical medical physics · 2024Article
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- An integrated solution of deep reinforcement learning for automatic IMRT treatment planning in non-small-cell lung cancer.Frontiers in oncology · 2023Article
- Feasibility and dosimetric evaluation of single- and multi-isocentre stereotactic body radiation therapy for multiple liver metastases.Frontiers in oncology · 2023Article
- Advances in Automated Treatment Planning.Seminars in radiation oncology · 2022Review
- Multi-institution model (big model) versus single-institution model of knowledge-based volumetric modulated arc therapy (VMAT) planning for prostate cancer.Scientific reports · 2022Article
- Training and validation of a knowledge-based dose-volume histogram predictive model in the optimisation of intensity-modulated proton and volumetric modulated arc photon plans for pleural mesothelioma patients.Radiation oncology (London, England) · 2022Article
- Influence of Beam Angle on Normal Tissue Complication Probability of Knowledge-Based Head and Neck Cancer Proton Planning.Cancers · 2022Article
- Using multi-centre data to train and validate a knowledge-based model for planning radiotherapy of the head and neck.Physics and imaging in radiation oncology · 2022Article
- Knowledge-based planning for multi-isocenter VMAT total marrow irradiation.Frontiers in oncology · 2022Article
- Artificial intelligence applications in intensity modulated radiation treatment planning: an overview.Quantitative imaging in medicine and surgery · 2021Review
- A comparison of two methodologies for radiotherapy treatment plan optimization and QA for clinical trials.Journal of applied clinical medical physics · 2021Article
4 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo evaluate in-silico the performance of a model-based optimization process for volumetric modulated arc therapy (RapidArc) applied to hepatocellular cancer treatments. PATIENTS AND
methods45 clinically accepted RA plans were selected to train a knowledge-based engine for the prediction of individualized dose-volume constraints. The model was validated on the same plans used for training (closed-loop) and on a set of other 25 plans not used for the training (open-loop). Dose prescription, target size, localization in the liver and arc configuration were highly variable in both sets to appraise the power of generalization of the engine. Quantitative dose volume histogram analysis was performed as well as a pass-fail analysis against a set of 8 clinical dose-volume objectives to appraise the quality of the new plans.
resultsQualitative and quantitative equivalence was observed between the clinical and the test plans. The use of model-based optimization lead to a net improvement in the pass-rate of the clinical objectives compared to the plans originally optimized with standard methods (this pass-rate is the frequency of cases where the objectives are respected vs. the cases where constraints are not fulfilled). The increase in the pass-rate resulted of 2.0%, 0.9% and 0.5% in a closed-loop and two different open-loop validation experiments.
conclusionsA knowledge-based engine for the optimization of RapidArc plans was tested and lead to clinically acceptable plans in the case of hepatocellular cancer radiotherapy. More studies are needed before a broad clinical use.
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