Improving dose coverage of tumor periphery in radiotherapy of lung cancer through the use of flattening filter free beams

通过使用无平坦滤波器光束改善肺癌放射治疗中肿瘤周边的剂量覆盖

• 批准号: 9218692
• 负责人: Oleg Vassiliev
• 金额: $ 8万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9218692
• 关键词: Achievement; Affect; Algorithms; Anatomy; Biological Models; Cancer Patient; Data; Data Reporting; Data Set; Dose; Electron Transport; Electrons; Feasibility Studies; Image; Location; Lung; Lung Neoplasms; Malignant neoplasm of lung; Methodology; Modeling; Morphologic artifacts; Motion; Patient Selection; Patients; Peripheral; Phase; Photons; Planning Techniques; Probability; Radiation; Radiation therapy; Recurrence; Relapse; Research; Resolution; Risk; Selection Criteria; Spatial Distribution; Structure of parenchyma of lung; Surface; Techniques; Testing; Thinness; Translating; X-Ray Computed Tomography; base; cancer radiation therapy; clinical practice; clinically significant; cohort; density; image registration; improved; respiratory; standard care; success; time use; treatment planning; tumor; virtual

PROJECT SUMMARY The proposed study concerns radiation therapy for lung cancer. Lung tumors are usually surrounded, at least partially, by lung tissue that has a much lower density than the tumor. In external photon-beam radiotherapy, the lung-tumor interface is characterized by disequilibrium of secondary electrons that results in dose build-up and build-down regions near the tumor surface, where the beam enters and exits it. This translates into underdosage of tumor surface, and potentially compromises tumor control. In routine treatment planning this underdosage is largely undetected. Even though presently 4DCT images are acquired for most lung cancer patients, these images are used primarily to determine the extent of tumor motion. Dose calculations, on the other hand, are typically performed on a CT image averaged over the respiratory cycle. Because the tumor moves, such averaging smears the lung-tumor interface, leading to dose calculation errors in superficial regions of the tumor that may compromise treatment. The proposed study will first evaluate the magnitude of the underdosage and its clinical significance in terms of tumor control probability. This will be accomplished by utilizing a methodology that offers two important improvements over the standard treatment planning techniques: (a) dose distributions will be calculated separately for each of 10 phases of the respiratory cycle, and then combined so as to evaluate the spatial distribution of the dose delivered to the tumor; (b) calculations will be performed with Monte Carlo, the most advanced dose calculation algorithm available. Second, we will test the hypothesis that the use of flattening filter-free beams improves tumor control probability. Previous treatment planning studies have demonstrated that such treatments are feasible and that dose in build-up regions is higher owing to a softer photon spectrum. Our research strategy has three specific aims: (1) evaluate the magnitude of the underdosage in a simple model of a lung tumor, namely a spherical static tumor surrounded by lung tissue, calculating doses with Monte Carlo; (2) evaluate the magnitude of the underdosage for a cohort of 15 previously treated patients, calculating dose distributions retrospectively with Monte Carlo for each of the 10 respiratory phases separately, and then combining them; (3) compare predicted tumor control probabilities between treatments with conventional photon beams and flattening filter free beams. For this purpose, for each patient in the cohort a new treatment plan will be generated using flattening filter free beams.

项目概要 拟议的研究涉及肺癌的放射治疗。肺部肿瘤通常至少被包围 部分是由密度比肿瘤低得多的肺组织造成的。在体外光子束放射治疗中， 肺-肿瘤界面的特点是二次电子不平衡，导致剂量累积 以及肿瘤表面附近的构建区域，光束在此区域进出。这翻译成 肿瘤表面剂量不足，并可能损害肿瘤控制。 在常规治疗计划中，这种剂量不足基本上未被发现。尽管目前 4DCT 图像 对于大多数肺癌患者来说，这些图像主要用于确定肿瘤的范围 运动。另一方面，剂量计算通常是在平均 CT 图像上进行的。 呼吸循环。由于肿瘤移动，这种平均会涂抹肺-肿瘤界面，导致剂量 肿瘤表面区域的计算错误可能会影响治疗。 拟议的研究将首先评估剂量不足的程度及其临床意义： 肿瘤控制概率。这将通过利用提供两个重要的方法来实现 标准治疗计划技术的改进：(a) 将计算剂量分布 分别针对呼吸周期的 10 个阶段中的每个阶段，然后进行组合以评估空间 递送至肿瘤的剂量分布； (b) 计算将使用蒙特卡罗进行， 提供先进的剂量计算算法。其次，我们将检验以下假设： 平坦的无滤波器光束提高了肿瘤控制概率。先前的治疗计划研究已 表明这种治疗方法是可行的，并且由于较软的环境，聚集区的剂量较高 光子光谱。 我们的研究策略有三个具体目标：（1）以简单的方式评估剂量不足的程度 肺肿瘤模型，即被肺组织包围的球形静态肿瘤，计算剂量 蒙特卡洛； (2) 评估 15 名既往治疗患者的队列的剂量不足程度 患者，使用蒙特卡罗回顾性计算 10 个呼吸阶段中每个阶段的剂量分布 分开，然后组合； (3)比较预测的肿瘤控制概率 使用传统光子束和无滤光片平坦光束进行治疗。为此，对于每个 对于队列中的患者，将使用平坦的无滤波器光束生成新的治疗计划。