Lung Tumor Motion Behavior Analysis Using 4DCT

描述（由申请人提供）：图像引导放射治疗（IGRT）可以潜在地提高下腹和肺部肿瘤的治疗效果，这些肿瘤经历呼吸诱导的运动。然而，它的成功很大程度上取决于对肿瘤运动特征的充分理解和对肿瘤未来位置的准确预测。这样的预测构成了一个具有挑战性的问题，因为呼吸诱导的肿瘤运动是复杂的和患者特异性的。尤其具有挑战性的是患有晚期肺病或呼吸严重受损的患者。他们的肿瘤运动可能非常不稳定周期和振幅不均匀，等等。本研究的总体目标是改善移动性肺肿瘤的放射治疗。这项建议有两个具体目标。第一个具体目标包括肿瘤运动和患者呼吸的行为分析。肿瘤的运动将在数学上通过定义参数来表征，这些参数在治疗期间随时间以及在治疗过程中累积对其运动进行分类。个体患者的呼吸行为将通过定义运动属性（例如，振幅、频率、速度）和它们在各种患者生物医学（例如解剖和生理）条件下的各种呼吸状态（例如呼气、吸气、呼气结束）之间的关系来建模。第二个具体目标包括肿瘤运动预测模型的发展。基于先前的运动模式，将建立一个预测肿瘤未来运动行为的统计模型，并在实时放射治疗期间动态调整。我们将探讨一个加权概率的隐马尔可夫模型。该模型有望准确预测呼吸诱导的肿瘤运动，从而实现真正的实时IGRT。由于呼吸诱导的肿瘤运动尚未完全表征，并且难以预测肺各部位的肿瘤运动，因此本研究具有创新性。我们的跨学科研究团队独特地结合了不同范围的数据管理，物理支持和临床专业知识，以达到这项研究的明确结果。公共卫生相关性：肺癌是美国第一大癌症杀手，5年生存率仅为15%，因此提出的肺癌治疗研究具有重要意义。本提案中描述的跨学科和转化研究将导致移动性肺肿瘤放射癌症治疗的改进，并使此类疾病的真正实时图像引导放射治疗（IGRT）成为可能。

DESCRIPTION (provided by applicant): Image-guided radiation therapy (IGRT) can potentially increase treatment effectiveness for tumors of the lower abdomen and lungs that undergo respiration-induced motion. However, its success largely depends upon an adequate understanding of tumor motion characteristics and accurate prediction of tumor position at some point in the future. Such predictions constitute a challenging problem since respiration-induced tumor motion is complicated and patient-specific. Particularly challenging are patients having advanced lung disease or who have highly compromised breathing. Their tumor's motion may be highly erratic with non-uniform period and amplitude, et cetera. The overall objective of the work proposed in this application is to improve the radiation treatment of moving lung tumors. There are two specific aims under this proposal. The first specific aim covers the behavior analysis of tumor motion and patient breathing. The motion of a tumor will be mathematically characterized by defining parameters that categorize its movement with time during a treatment fraction and also cumulatively over the course of the treatment. Individual patient breathing behavior will be modeled by defining motion properties (e.g., amplitude, frequency, velocity) and their relationships between various breathing states (e.g. exhale, inhale, end of exhale) under various patient biomedical (such as anatomical and physiological) conditions. The second specific aim covers the development of a predictive model for tumor motion. A statistical model for predicting future movement behavior of a tumor based on previous motion patterns will be built and dynamically adjusted during real-time radiation treatment. A Hidden Markov Model with weighted probabilities will be explored. The model is expected to accurately predict respiratory-induced tumor motion to allow for true real-time IGRT. The proposed research is innovative since respiration-induced tumor motion has not been fully characterized and the prediction of tumor motion in various parts of the lung is difficult. Our interdisciplinary team of investigators uniquely combines the diverse range of data management, physics support, and clinical expertise needed to reach a definitive outcome for this research. PUBLIC HEALTH RELEVANCE: The proposed research on lung cancer treatment is of great significance since lung cancer is the number one cancer killer in the United States and the five year survival rate is only 15%. The interdisciplinary and translational research described in this proposal will lead to improvement in radiation cancer treatment of moving lung tumors and true real-time image guided radiation therapy (IGRT) for such disease will be made possible.