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Functional-guided radiotherapy using knowledge-based planning
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DOI：10.1016/j.radonc.2018.03.025.png)
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摘要
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Background and purpose: There are two significant challenges when implementing functional-guided radiotherapy using 4DCT-ventilation imaging: (1) lack of knowledge of realistic patient specific dosimetric goals for functional lung and (2) ensuring consistent plan quality across multiple planners. Knowledge-based planning (KBP) is positioned to address both concerns. Material and methods: A KBP model was created from 30 previously planned functional-guided lung patients. Standard organs at risk (OAR) in lung radiotherapy and a ventilation contour delineating areas of high ventilation were included. Model validation compared dose-metrics to standard OARs and functional dose-metrics from 20 independent cases that were planned with and without KBP. Results: A significant improvement was observed for KBP optimized plans in V20Gy and mean dose to functional lung (p = 0.005 and 0.001, respectively), V20Gy and mean dose to total lung minus GTV (p = 0.002 and 0.01, respectively), and mean doses to esophagus (p = 0.005). Conclusion: The current work developed a KBP model for functional-guided radiotherapy. Modest, but statistically significant, improvements were observed in functional lung and total lung doses. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Knowledge-based planning
Functional-guided radiotherapy
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IF:
5.3
论文数:
2.1W
被引数:
2.4W
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