Yao Rui, Templeton Alistair K, Liao Yixiang, Turian Julius V, Kiel Krystyna D, Chu James C H
Department of Radiation Oncology, Rush University Medical Center, 500 S. Paulina Street, Chicago, IL.
Department of Radiation Oncology, Rush University Medical Center, 500 S. Paulina Street, Chicago, IL.
Brachytherapy. 2014 Jul-Aug;13(4):352-60. doi: 10.1016/j.brachy.2013.10.013. Epub 2013 Dec 17.
To validate an in-house optimization program that uses adaptive simulated annealing (ASA) and gradient descent (GD) algorithms and investigate features of physical dose and generalized equivalent uniform dose (gEUD)-based objective functions in high-dose-rate (HDR) brachytherapy for cervical cancer.
Eight Syed/Neblett template-based cervical cancer HDR interstitial brachytherapy cases were used for this study. Brachytherapy treatment plans were first generated using inverse planning simulated annealing (IPSA). Using the same dwell positions designated in IPSA, plans were then optimized with both physical dose and gEUD-based objective functions, using both ASA and GD algorithms. Comparisons were made between plans both qualitatively and based on dose-volume parameters, evaluating each optimization method and objective function. A hybrid objective function was also designed and implemented in the in-house program.
The ASA plans are higher on bladder V75% and D2cc (p=0.034) and lower on rectum V75% and D2cc (p=0.034) than the IPSA plans. The ASA and GD plans are not significantly different. The gEUD-based plans have higher homogeneity index (p=0.034), lower overdose index (p=0.005), and lower rectum gEUD and normal tissue complication probability (p=0.005) than the physical dose-based plans. The hybrid function can produce a plan with dosimetric parameters between the physical dose-based and gEUD-based plans. The optimized plans with the same objective value and dose-volume histogram could have different dose distributions.
Our optimization program based on ASA and GD algorithms is flexible on objective functions, optimization parameters, and can generate optimized plans comparable with IPSA.
验证一个使用自适应模拟退火（ASA）和梯度下降（GD）算法的内部优化程序，并研究基于物理剂量和广义等效均匀剂量（gEUD）的目标函数在宫颈癌高剂量率（HDR）近距离放射治疗中的特征。
本研究使用了8例基于Syed/Neblett模板的宫颈癌HDR组织间近距离放射治疗病例。首先使用逆向计划模拟退火（IPSA）生成近距离放射治疗计划。然后使用ASA和GD算法，基于物理剂量和gEUD的目标函数，对IPSA中指定的相同驻留位置进行计划优化。对计划进行定性比较，并基于剂量体积参数进行比较，评估每种优化方法和目标函数。还设计并在内部程序中实现了一个混合目标函数。
与IPSA计划相比**，**ASA计划的膀胱V75%和D2cc更高（p = 0.034），直肠V75%和D2cc更低（p = 0.034）。ASA和GD计划无显著差异。与基于物理剂量的计划相比，基于gEUD的计划具有更高的均匀性指数（p = 0.034）、更低的过量指数（p = 0.005）以及更低的直肠gEUD和正常组织并发症概率（p = 0.005）。混合函数可以生成一个剂量学参数介于基于物理剂量和基于gEUD的计划之间的计划。具有相同目标值和剂量体积直方图的优化计划可能具有不同的剂量分布。
我们基于ASA和GD算法的优化程序在目标函数、优化参数方面具有灵活性，并且可以生成与IPSA相当的优化计划。
