ArticleJournal of applied clinical medical physics2020
Incorporating biological modeling into patient-specific plan verification.
Article in Journal of applied clinical medical physics, 2020. 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.
- Introducing a novel prediction index for treatment quality of gamma knife and stereotactic-based radiotherapy: Nabaa Efficiency Index (Journal of applied clinical medical physics · 2026Article
- A prescription-free, radiobiology-based framework for automated VMAT planning: A feasibility study in primary prostate cancer radiotherapy.Medical physics · 2026Article
- Impact of iso-dose levels on pre-treatment quality assurance in volumetric modulated arc therapy.Radiological physics and technology · 2025Article
- Impact of Reference Selection on Gamma Analysis in Patient-Specific Quality Assurance for Monte Carlo-Based Treatment Planning Systems.Asian Pacific journal of cancer prevention : APJCP · 2024Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeDose-volume histogram (DVH) measurements have been integrated into commercially available quality assurance systems to provide a metric for evaluating accuracy of delivery in addition to gamma analysis. We hypothesize that tumor control probability and normal tissue complication probability calculations can provide additional insight beyond conventional dose delivery verification methods.
methodsA commercial quality assurance system was used to generate DVHs of treatment plan using the planning CT images and patient-specific QA measurements on a phantom. Biological modeling was performed on the DVHs produced by both the treatment planning system and the quality assurance system.
resultsThe complication-free tumor control probability, P
conclusionDose measurements on a phantom can be used for pretreatment estimation of tumor control and normal tissue complication probabilities. Results in this study show how biological modeling can be used to provide additional insight about accuracy of delivery during pretreatment verification.
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