The dosimetric impact of trade-off optimization in high-quality Craniospinal Irradiation plans based on volumetric modulated arc therapy technique

弧(几何) 放射治疗 核医学 医学 医学物理学 放射治疗计划 辐照 放射科 数学 物理 核物理学 几何学
作者
Subhra Snigdha Biswal,Biplab Sarkar,Monika Goyal,T Ganesh
出处
期刊:Medical Dosimetry [Elsevier]
标识
DOI:10.1016/j.meddos.2024.07.006
摘要

This study assesses the dosimetric effectiveness of the commercial trade-off optimization (TO) module in comparison to iterative optimization for volumetric modulated arc therapy (VMAT) in craniospinal irradiation technique.Fifteen patients who had previously undergone VMAT-based craniospinal irradiation (CSI) using manual optimization (TP) underwent re-optimization with trade-off optimization (MCO). All patients were treated using the Halcyon-E O-ring linear accelerator, with maximum field size of 28×28 cm², a 6MV unflattened beam, and adjacent isocenter field overlap of 10 cm. Plans were compared based on PTV dose coverage (D95%), maximum dose (Dmax), conformity index (CI), heterogeneity index (HI), maximum and mean dose to serial and parallel organs, respectively. Statistical evaluation was conducted using paired sample t-tests. The PTVD95% for TO and MCO plans were 98.0% ± 1.0% and 97.4% ± 0.7%, respectively. In the same sequence, HIs were 1.06 ± 0.01 and 1.07 ± 0.01. CIs for both arms were 0.9 ± 0.0 and its variation was statistically significant (p = 0.027). The differences in dose for bilateral cochlea and left optic nerves were statistically significant (0.022≤ p ≤0.049). The ΔDmax for serial organs and mean dose for parallel organs did not exceed 1%, except for the bilateral optic nerve, mandible, oral cavity, right parotid, and stomach. No parallel organ showed a statistically significant dose variation. Clinically significant reductions in dose were noted for three organs; the average dose reduction in MCO plans for bilateral optic nerves was 3.9%, and for the larynx, it was 8.5%. In this study, trade-off optimization did not demonstrate any significant improvement over the iteratively optimized plans, primarily because the planners were highly skilled and could already generate high-quality plans using iterative optimization alone. However, this finding may not necessarily apply universally to all treatment planners or clinical settings.

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