Influence of Beam Angle on Normal Tissue Complication Probability of Knowledge-Based Head and Neck Cancer Proton Planning

被引:1
|
作者
Hytonen, Roni [1 ]
Vanderstraeten, Reynald [2 ]
Dahele, Max [3 ,4 ]
Verbakel, Wilko F. A. R. [3 ,4 ]
机构
[1] Varian Med Syst Finland, Helsinki 00270, Finland
[2] Varian Med Syst Belgium, B-1831 Diegem, Belgium
[3] Amsterdam UMC Locat Vrije Univ Amsterdam, Dept Radiat Oncol, NL-1081 HV Amsterdam, Netherlands
[4] Canc Ctr Amsterdam, NL-1081 HV Amsterdam, Netherlands
关键词
knowledge-based planning; intensity-modulated proton therapy; normal tissue complication probability; automated optimization; MODULATED ARC THERAPY; MODEL-BASED SELECTION; OPTIMIZATION; RISK;
D O I
10.3390/cancers14122849
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Simple Summary Knowledge-based treatment planning (KBP) solutions can be used to assist in the planning process by automatically generating patient-specific optimization objectives. A KBP model is typically derived from treatment plans with a similar planning methodology, for example beam angles. An end-user might deviate from this methodology for a variety of reasons. The effect of such deviations on KBP plan quality has not been widely explored. We therefore studied this using a human-interaction free proton planning solution to create comparative plans with the default angles used when building the model, and altered beam angle arrangements. Because normal tissue complication probability (NTCP) can be used to select patients for proton therapy, this was used as the primary outcome metric for plan quality. The results show that the beam angle and number of beams only had a small effect on the plan NTCP. This suggests that the model is robust to the various beam arrangements within the range described in this analysis, although a method that automatically further adapts the KBP planning objectives further decreased the NTCP by 1-3%. Knowledge-based planning solutions have brought significant improvements in treatment planning. However, the performance of a proton-specific knowledge-based planning model in creating knowledge-based plans (KBPs) with beam angles differing from those used to train the model remains unexplored. We used a previously validated RapidPlanPT model and scripting to create nine KBPs, one with default and eight with altered beam angles, for 10 recent oropharynx cancer patients. The altered-angle plans were compared against the default-angle ones in terms of grade 2 dysphagia and xerostomia normal tissue complication probability (NTCP), mean doses of several organs at risk, and dose homogeneity index (HI). As KBP could be suboptimal, a proof of principle automatic iterative optimizer (AIO) was added with the aim of reducing the plan NTCP. There were no statistically significant differences in NTCP or HI between default- and altered-angle KBPs, and the altered-angle plans showed a <1% reduction in NTCP. AIO was able to reduce the sum of grade 2 NTCPs in 66/90 cases with mean a reduction of 3.5 +/- 1.8%. While the altered-angle plans saw greater benefit from AIO, both default- and altered-angle plans could be improved, indicating that the KBP model alone was not completely optimal to achieve the lowest NTCP. Overall, the data showed that the model was robust to the various beam arrangements within the range described in this analysis.
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页数:12
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