ArticlePhysics and imaging in radiation oncology2021
Characterization of automatic treatment planning approaches in radiotherapy.
Article in Physics and imaging in radiation oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 24 citations in OpenAlex.
- Automated planning in image-guided adaptive brachytherapy for cervical cancer: Impact analysis on patient workflows in high-volume centers.Journal of contemporary brachytherapy · 2026Article
- Objective-function-guided automated VMAT planning reduces OAR dose, low-dose exposure, and inter-planner variability in breast radiotherapy.Scientific reports · 2026Article
- Development of automated whole breast 3D planning with tangential fields.Journal of applied clinical medical physics · 2025Article
- Implementing Remote Radiotherapy Planning to Increase Patient Flow at a Johannesburg Academic Hospital, South Africa: Protocol for a Prospective Feasibility Study.JMIR research protocols · 2025Article
- MR-linac: role of artificial intelligence and automation.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Feasibility and impact of knowledge-based automated radiotherapy treatment planning in low- and middle-income countries.Ecancermedicalscience · 2025Article
- Geometrical and dosimetrical evaluation of different interpretations of a european consensus delineation guideline for the internal mammary lymph node chain in breast cancer patients.Physics and imaging in radiation oncology · 2024Article
- Clinical outcomes of pelvic bone marrow sparing radiotherapy for cervical cancer: A systematic review andClinical and translational radiation oncology · 2024Review
- Artificial intelligence applications in prostate cancer.Prostate cancer and prostatic diseases · 2024Review
- Full daily re-optimization improves plan quality during online adaptive radiotherapy.Physics and imaging in radiation oncology · 2024Article
- A geometry and dose-volume based performance monitoring of artificial intelligence models in radiotherapy treatment planning for prostate cancer.Physics and imaging in radiation oncology · 2023Article
- Updating approach for lexicographic optimization-based planning to improve cervical cancer plan quality.Discover oncology · 2023Article
- OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines.Physics in medicine and biology · 2022Article
- Organ-at-risk dose prediction using a machine learning algorithm: Clinical validation and treatment planning benefit for lung SBRT.Journal of applied clinical medical physics · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeAutomatic approaches are widely implemented to automate dose optimization in radiotherapy treatment planning. This study systematically investigates how to configure automatic planning in order to create the best possible plans. MATERIALS AND
methodsAutomatic plans were generated using protocol based automatic iterative optimization. Starting from a simple automation protocol which consisted of the constraints for targets and organs at risk (OAR), the performance of the automatic approach was evaluated in terms of target coverage, OAR sparing, conformity, beam complexity, and plan quality. More complex protocols were systematically explored to improve the quality of the automatic plans. The protocols could be improved by adding a dose goal on the outer 2 mm of the PTV, by setting goals on strategically chosen subparts of OARs, by adding goals for conformity, and by limiting the leaf motion. For prostate plans, development of an automated post-optimization procedure was required to achieve precise control over the dose distribution. Automatic and manually optimized plans were compared for 20 head and neck (H&N), 20 prostate, and 20 rectum cancer patients.
resultsBased on simple automation protocols, the automatic optimizer was not always able to generate adequate treatment plans. For the improved final configurations for the three sites, the dose was lower in automatic plans compared to the manual plans in 12 out of 13 considered OARs. In blind tests, the automatic plans were preferred in 80% of cases.
conclusionsWith adequate, advanced, protocols the automatic planning approach is able to create high-quality treatment plans.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.