Department of Radiation Oncology, University of Miami, 1475 NW 12th Ave, Suite 1500, Miami, FL 33136, United States.
Radiation Oncology and Diagnostic Imaging, H. Lee Moffitt Cancer Center, 12902 Magnolia Dr., Tampa, FL 33612, United States.
Phys Med. 2018 Oct;54:77-83. doi: 10.1016/j.ejmp.2018.06.635. Epub 2018 Oct 1.
The voxels in a CT data sets contain density information. Besides its use in dose calculation density has no other application in modern radiotherapy treatment planning. This work introduces the use of density information by integral dose minimization in radiotherapy treatment planning for head-and-neck squamous cell carcinoma (HNSCC).
Eighteen HNSCC cases were studied. For each case two intensity modulated radiotherapy (IMRT) plans were created: one based on dose-volume (DV) optimization, and one based on integral dose minimization (Energy hereafter) inverse optimization. The target objective functions in both optimization schemes were specified in terms of minimum, maximum, and uniform doses, while the organs at risk (OAR) objectives were specified in terms of DV- and Energy-objectives respectively. Commonly used dosimetric measures were applied to assess the performance of Energy-based optimization. In addition, generalized equivalent uniform doses (gEUDs) were evaluated. Statistical analyses were performed to estimate the performance of this novel inverse optimization paradigm.
Energy-based inverse optimization resulted in lower OAR doses for equivalent target doses and isodose coverage. The statistical tests showed dose reduction to the OARs with Energy-based optimization ranging from ∼2% to ∼15%.
Integral dose minimization based inverse optimization for HNSCC promises lower doses to nearby OARs. For comparable therapeutic effect the incorporation of density information into the optimization cost function allows reduction in the normal tissue doses and possibly in the risk and the severity of treatment related toxicities.
CT 数据集的体素包含密度信息。除了在剂量计算中的应用外，密度在现代放射治疗计划中没有其他应用。本研究旨在通过头颈部鳞状细胞癌（HNSCC）放射治疗计划中的积分剂量最小化来利用密度信息。
研究了 18 例 HNSCC 病例。对于每个病例，创建了两种调强放疗（IMRT）计划：一种基于剂量-体积（DV）优化，另一种基于积分剂量最小化（Energy 此后）逆优化。两种优化方案中的目标目标函数均以最小、最大和均匀剂量为指标，而危及器官（OAR）目标则分别以 DV 和 Energy 目标为指标。常用的剂量学指标用于评估 Energy 基优化的性能。此外，还评估了广义等效均匀剂量（gEUDs）。进行了统计分析以估计这种新的逆优化范例的性能。
基于 Energy 的逆优化导致等效靶剂量和等剂量覆盖的 OAR 剂量降低。统计检验表明，基于 Energy 的优化对 OAR 剂量的降低范围为约 2%至约 15%。
基于 HNSCC 的积分剂量最小化逆优化有望降低附近 OAR 的剂量。对于可比的治疗效果，将密度信息纳入优化成本函数可降低正常组织剂量，并可能降低治疗相关毒性的风险和严重程度。
