ArticleJournal of applied clinical medical physics2022
Knowledge-based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer.
Article in Journal of applied clinical medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- AI-assisted VMAT planning incorporating deep learning-based dose prediction for head and neck cancer: feasibility of quality standardization and human intervention for irregular cases.Journal of applied clinical medical physics · 2026Article
- Article
- Deep-Learning-Based 3D Dose Distribution Prediction for VMAT Lung Cancer Treatment Using an Enhanced UNet3D Architecture with Composite Loss Functions.Bioengineering (Basel, Switzerland) · 2026Article
- Article
- PlanAct: An eclipse scripting API-based module embedding clinical optimization strategies for automated planning in locally advanced non-small cell lung cancer.Journal of applied clinical medical physics · 2025Article
- ADAPT-marketplace - A web-application for XML-based template sharing in radiation oncology.Journal of applied clinical medical physics · 2025Article
- An extension to the OVH concept for knowledge-based dose volume histogram prediction in lung tumor volumetric-modulated arc therapy.Journal of applied clinical medical physics · 2025Article
- Feasibility study of automatic radiotherapy treatment planning for cervical cancer using a large language model.Radiation oncology (London, England) · 2025Article
- Quality and mechanical efficiency of automated knowledge-based planning for volumetric-modulated arc therapy in head and neck cancer.Journal of applied clinical medical physics · 2025Article
- Uninvolved liver dose prediction in stereotactic body radiation therapy for liver cancer based on the neural network method.World journal of gastrointestinal oncology · 2024Article
- Article
- Knowledge-based versus deep learning based treatment planning for breast radiotherapy.Physics and imaging in radiation oncology · 2024Article
- Deep learning-based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers.Medical physics · 2023Article
- Implementation of a knowledge-based decision support system for treatment plan auditing through automation.Medical physics · 2023Article
- Automated evaluation for rapid implementation of knowledge-based radiotherapy planning models.Journal of applied clinical medical physics · 2023Article
- Implementation, Dosimetric Assessment, and Treatment Validation of Knowledge-Based Planning (KBP) Models in VMAT Head and Neck Radiation Oncology.Biomedicines · 2023Article
- Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?Diagnostics (Basel, Switzerland) · 2023Review
- Clinical acceptability of fully automated external beam radiotherapy for cervical cancer with three different beam delivery techniques.Medical physics · 2022Article
- Knowledge-based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer.Journal of applied clinical medical physics · 2022Article
- Effectiveness of RapidPlan in Combination with Multicriteria Optimization for Cervix Radiotherapy Planning.Journal of medical physicsArticle
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Authors and funding
10 authors.
Funding
Abstract
This study aimed to investigate the feasibility of using a knowledge-based planning technique to detect poor quality VMAT plans for patients with head and neck cancer. We created two dose-volume histogram (DVH) prediction models using a commercial knowledge-based planning system (RapidPlan, Varian Medical Systems, Palo Alto, CA) from plans generated by manual planning (MP) and automated planning (AP) approaches. DVHs were predicted for evaluation cohort 1 (EC1) of 25 patients and compared with achieved DVHs of MP and AP plans to evaluate prediction accuracy. Additionally, we predicted DVHs for evaluation cohort 2 (EC2) of 25 patients for which we intentionally generated plans with suboptimal normal tissue sparing while satisfying dose-volume limits of standard practice. Three radiation oncologists reviewed these plans without seeing the DVH predictions. We found that predicted DVH ranges (upper-lower predictions) were consistently wider for the MP model than for the AP model for all normal structures. The average ranges of mean dose predictions among all structures was 9.7 Gy (MP model) and 3.4 Gy (AP model) for EC1 patients. RapidPlan models identified 7 MP plans as outliers according to mean dose or D1% for at least one structure, while none of AP plans were flagged. For EC2 patients, 22 suboptimal plans were identified by prediction. While re-generated AP plans validated that these suboptimal plans could be improved, 40 out of 45 structures with predicted poor sparing were also identified by oncologist reviews as requiring additional planning to improve sparing in the clinical setting. Our study shows that knowledge-based DVH prediction models can be sufficiently accurate for plan quality assurance purposes. A prediction model built by a small cohort automatically-generated plans was effective in detecting suboptimal plans. Such tools have potential to assist the plan quality assurance workflow for individual patients in the clinic.
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