ArticleClinical and translational radiation oncology2016
Automatic treatment planning improves the clinical quality of head and neck cancer treatment plans.
Article in Clinical and translational radiation oncology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 111 citations in OpenAlex.
- Objective-function-guided automated VMAT planning reduces OAR dose, low-dose exposure, and inter-planner variability in breast radiotherapy.Scientific reports · 2026Article
- Parallel/Opposed: Automated treatment planning systems should be designed to minimize human intervention.Journal of applied clinical medical physics · 2026Article
- Intelligent Support for Radiotherapy: A Review of Clinical Applications for Large Language Models.Journal of clinical medicine · 2026Review
- From data to treatment plan: An AI-driven path for automated breast radiotherapy planning.Journal of applied clinical medical physics · 2026Article
- Can knowledge-based autoplanning keep up with advances in radiotherapy optimization for oropharyngeal cancer?Frontiers in oncology · 2026Article
- Knowledge-Based RapidPlan Volumetric Modulated Arc Therapy Model in Nasopharyngeal Carcinoma.Advances in radiation oncology · 2025Article
- Clinical workload profile of medical physics professionals at particle therapy Centers: a National Survey in Japan.Journal of radiation research · 2025Article
- Automated VMAT planning for short-course radiotherapy in locally advanced rectal cancer.PloS one · 2025Article
- Application and progress of artificial intelligence in radiation therapy dose prediction.Clinical and translational radiation oncology · 2024Review
- A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck.Journal of applied clinical medical physics · 2023Article
- Multi-isocenter VMAT craniospinal irradiation using feasibility dose-volume histogram-guided auto-planning technique.Journal of radiation research · 2023Article
- Feasibility and dosimetric evaluation of single- and multi-isocentre stereotactic body radiation therapy for multiple liver metastases.Frontiers in oncology · 2023Article
- Delineation uncertainties of tumour volumes on MRI of head and neck cancer patients.Clinical and translational radiation oncology · 2022Article
- A Novel Method for Improving VMAT Plan Quality and Deliverability.Asian Pacific journal of cancer prevention : APJCP · 2022Article
- Barriers and facilitators to clinical implementation of radiotherapy treatment planning automation: A survey study of medical dosimetrists.Journal of applied clinical medical physics · 2022Review
- Treatment plan comparison of proton vs photon radiotherapy for lower-grade gliomas.Physics and imaging in radiation oncology · 2021Article
- Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface.IEEE transactions on bio-medical engineering · 2021Article
- Fully automated treatment planning for MLC-based robotic radiotherapy.Medical physics · 2021Article
- Knowledge-based radiation treatment planning: A data-driven method survey.Journal of applied clinical medical physics · 2021Review
- Characterization of automatic treatment planning approaches in radiotherapy.Physics and imaging in radiation oncology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTreatment plans for head and neck (H&N) cancer are highly complex due to multiple dose prescription levels and numerous organs at risk (OARs) close to the target. The plan quality is inter-planner dependent since it is dependent on the skills and experience of the dosimetrist. This study presents a blinded prospective clinical comparison of automatic (AU) and manually (MA) generated H&N VMAT plans made for clinical use.
methodsMA and AU plans were generated for 30 consecutive patients in Pinnacle
resultsFor twenty-nine out of the thirty patients the AU plan was chosen for treatment. Target doses were more homogenous with the AU plans and the OAR doses were significantly reduced, between 0.5 and 6.5 Gy. The average operator time spent on creating a manual plan was 64 min which was halved by Autoplan. The AU plans were more modulated as illustrated by an increase in MUs, which might cause the slightly lower pass rate of 97.7% in the ArcCheck measurements.
conclusionsTarget doses were similar between MA and AU plan, while AU plans spared all OAR considerably better than the MA plans.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.