ArticleRadiation oncology (London, England)2015
A broad scope knowledge based model for optimization of VMAT in esophageal cancer: validation and assessment of plan quality among different treatment centers.
Article in Radiation oncology (London, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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61 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
- Knowledge-based treatment planning in breast cancer radiotherapy: comparing different machine learning algorithms.Physical and engineering sciences in medicine · 2026Article
- Dosimetric impact of knowledge-based automated planning using HyperArc for angiosarcoma of the scalp.Physical and engineering sciences in medicine · 2026Article
- Desargues cloud TPS: a cloud-based automatic radiation treatment planning system for IMRT.Biomedical engineering online · 2026Article
- Fully automated intensity-modulated radiotherapy plans for rectal cancer based on deep learning predictions of three-dimensional dose distributions.Quantitative imaging in medicine and surgery · 2025Article
- Systematic implementation of rapidplan for prostate cancer: toward a unified knowledge-based planning model.Scientific reports · 2025Article
- Evaluation of deep learning-based deliverable VMAT plan generated by prototype software for automated planning for prostate cancer patients.Journal of radiation research · 2023Article
- Article
- Dosimetric potential of knowledge-based planning model trained with HyperArc plans for brain metastases.Journal of applied clinical medical physics · 2023Article
- Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors.Radiation oncology (London, England) · 2022Article
- Evaluation of auto-planning in VMAT for locally advanced nasopharyngeal carcinoma.Scientific reports · 2022Article
- Artificial Intelligence in Radiation Therapy.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Utilizing pre-determined beam orientation information in dose prediction by 3D fully-connected network for intensity modulated radiotherapy.Quantitative imaging in medicine and surgery · 2021Article
- Artificial intelligence applications in intensity modulated radiation treatment planning: an overview.Quantitative imaging in medicine and surgery · 2021Review
- Deep learning method for prediction of patient-specific dose distribution in breast cancer.Radiation oncology (London, England) · 2021Article
- Knowledge-based radiation treatment planning: A data-driven method survey.Journal of applied clinical medical physics · 2021Review
- Tree-based exploration of the optimization objectives for automatic cervical cancer IMRT treatment planning.The British journal of radiology · 2021Article
- Article
- Knowledge-based planning using pseudo-structures for volumetric modulated arc therapy (VMAT) of postoperative uterine cervical cancer: a multi-institutional study.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2021Article
- Adjuvant Radiotherapy of Involved Field versus Elective Lymph Node in Patients with Operable Esophageal Squamous Cell Cancer: A Single Institution Prospective Randomized Controlled Study.Journal of Cancer · 2021Article
1 more citing papers are in PubMed but not listed here.
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8 authors.
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Abstract
backgroundTo evaluate the performance of a broad scope model-based optimisation process for volumetric modulated arc therapy applied to esophageal cancer. METHODS AND MATERIALS: A set of 70 previously treated patients in two different institutions, were selected to train a model for the prediction of dose-volume constraints. The model was built with a broad-scope purpose, aiming to be effective for different dose prescriptions and tumour localisations. It was validated on three groups of patients from the same institution and from another clinic not providing patients for the training phase. Comparison of the automated plans was done against reference cases given by the clinically accepted plans.
resultsQuantitative improvements (statistically significant for the majority of the analysed dose-volume parameters) were observed between the benchmark and the test plans. Of 624 dose-volume objectives assessed for plan evaluation, in 21 cases (3.3 %) the reference plans failed to respect the constraints while the model-based plans succeeded. Only in 3 cases (<0.5 %) the reference plans passed the criteria while the model-based failed. In 5.3 % of the cases both groups of plans failed and in the remaining cases both passed the tests.
conclusionsPlans were optimised using a broad scope knowledge-based model to determine the dose-volume constraints. The results showed dosimetric improvements when compared to the benchmark data. Particularly the plans optimised for patients from the third centre, not participating to the training, resulted in superior quality. The data suggests that the new engine is reliable and could encourage its application to clinical practice.
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