ArticleFrontiers in oncology2024
Enhancing dosimetric practices through knowledge-based predictive models: a case study on VMAT prostate irradiation.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 2 citations in OpenAlex.
- Systematic implementation of rapidplan for prostate cancer: toward a unified knowledge-based planning model.Scientific reports · 2025Article
- A comparative analysis of deep learning architectures with data augmentation and multichannel input for locoregional breast cancer radiotherapy.Journal of applied clinical medical physics · 2025Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
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Abstract
Introduction: Acquisition of dosimetric knowledge by radiation therapy planners is a protracted and complex process. This study delves into the impact of empirical predictive models based on the knowledge-based planning (KBP) methodology, aimed at detecting suboptimal results and homogenizing and improving existing practices for prostate cancer. Moreover, the dosimetric effect of implementing these models into routine clinical practice was also assessed. Materials and methods: Based on the KBP method, we analyzed 25 prostate treatment plans performed using VMAT by expert operators, aiming to correlate dose indicators with patient geometry. The Results: Using linear regression, we identified strong geometric predictive factors for the peritoneal cavity, rectum, and bladder ( Conclusions: The KBP methodology established a robust and personalized predictive model for dose estimation to organs at risk in prostate cancer. Implementing the model resulted in improved sparing of these organs. Notably, it yields a solid foundation for harmonizing dosimetric practices, alerting us to suboptimal results, and improving our knowledge.
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