ArticleJournal of applied clinical medical physics2025
An extension to the OVH concept for knowledge-based dose volume histogram prediction in lung tumor volumetric-modulated arc therapy.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Knowledge-based radiation therapy treatment planning decision support system for head and neck cancer utilizing multi-organ constellation matching.International journal of computer assisted radiology and surgery · 2026Article
- A hybrid cluster-then-predict machine learning radiotherapy knowledge-based planning framework for similarity matching using holistic target-OAR constellation geometry.Radiation oncology (London, England) · 2026Article
- OVH-guided planning for superior heart and lung sparing in breast cancer radiotherapy.Journal of applied clinical medical physics · 2026Article
- 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
- Three-dimensional Hierarchically Dense U-net Architecture for Knowledge-based Dose Prediction in Head and Neck Radiotherapy.Journal of medical physicsArticle
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5 authors.
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Abstract
purposeVolumetric-modulated arc therapy (VMAT) treatment planning allows a compromise between a sufficient coverage of the planning target volume (PTV) and a simultaneous sparing of organs-at-risk (OARs). Particularly in the case of lung tumors, deciding whether it is possible or worth spending more time on further improvements of a treatment plan is difficult. Therefore, this work aims to develop a knowledge-based, structure-dependent, automated dose volume histogram (DVH) prediction module for lung tumors.
methodsThe module is based on comparing geometric relationships between the PTV and the surrounding OARs. Therefore, treatment plan and structure data of 106 lung cancer cases, each treated in 28 fractions and 180 cGy/fx, were collected. To access the spatial information, a two-dimensional metric named overlap volume histogram (OVH) was used. Due to the rotational symmetry of the OVH and the typically coplanar setup of the VMAT technique, OVH is complemented by the so-called overlap-z-histogram (OZH). A set of achievable DVHs is predicted by identifying plans in the database with similar OVH and OZH. By splitting the dataset into a test set of 22 patients and a training set of 84 patients, the prediction capability of the OVH-OZH combination was evaluated. For comparison between the predicted and achieved DVH curves the coefficient of determination R
resultsThe total lung showed strong linearity between predicted and achieved DVH curves for the OVH-OZH combination, resulting in a
conclusionThe combination of OZH and OVH was suitable for building a knowledge-based automated DVH prediction module. Implementing this method into the clinical workflow of treatment planning will contribute to advancing the quality of VMAT plans.
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