ArticleRadiation oncology (London, England)2021
Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer.
Article in Radiation oncology (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- The impact of setup errors on dose distribution in cervical cancer radiotherapy and the margin from CTV to PTV.Journal of cancer research and clinical oncology · 2024Article
- [Quantitative evaluation of radiotherapy plan in precise external beam radiotherapy process management for cervical cancer].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- Evaluation of auto-planning in VMAT for locally advanced nasopharyngeal carcinoma.Scientific reports · 2022Article
- 'Line' Constraints Optimization for Improved Dose Distribution in Locally Recurrent Nasopharyngeal Carcinoma Using Knowledge-Based Planning.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
background and purposeTo explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer. METHODS AND MATERIALS: The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method. The reliability and efficiency of knowledge-based planning (KBP) using this highly refined model in improving the consistency and quality of the VMAT plans were also evaluated.
resultsThe prediction ability was reinforced with the increased number of refinements in terms of normal tissue sparing. With enhanced prediction accuracy, more than 60% of automatic plan-6 (AP-6) plans (22/35) can be directly approved for clinical treatment without any manual revision. The plan quality scores for clinically approved plans (CPs) and manual plans (MPs) were on average 89.02 ± 4.83 and 86.48 ± 3.92 (p < 0.001). Knowledge-based planning significantly reduced the D
conclusionThe proposed model evolution method provides a practical way for the KBP to enhance its prediction ability with minimal human intervene. This highly refined prediction model can better guide KBP in improving the consistency and quality of the VMAT plans.
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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.