Lung Tumor Motion Behavior Analysis Using 4DCT

使用 4DCT 进行肺肿瘤运动行为分析

• 批准号: 7533702
• 负责人: HUANMEI WU
• 金额: $ 18.65万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-15 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7533702
• 关键词: Abdomen; Behavior; Breathing; Characteristics; Clinical; Collimator; Condition; Data; Development; Disease; Environment; Exhalation; Frequencies; Future; Image; Implant; Indiana; Individual; Lead; Left; Location; Lung; Lung Neoplasms; Lung diseases; Malignant Neoplasms; Malignant neoplasm of lung; Methods; Modeling; Motion; Movement; Normal tissue morphology; Numbers; Outcome; Outcomes Research; Patients; Pattern; Physics; Physiological; Positioning Attribute; Probability; Property; Public Health; Radiation; Radiation therapy; Range; Research; Research Personnel; Resources; Respiration; Statistical Models; Survival Rate; Time; Translational Research; Treatment Effectiveness; Tumor Markers; United States; Universities; Variant; Weight; Work; base; cancer therapy; data management; improved; innovation; irradiation; markov model; predictive modeling; respiratory; size; success; tumor

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.

描述（由申请人提供）：图像引导放射治疗（IGRT）可能会增加下腹部和肺部发生呼吸诱导运动的肿瘤的治疗有效性。然而，它的成功在很大程度上取决于对肿瘤运动特性的充分理解和对未来某个时刻肿瘤位置的准确预测。这种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。特别具有挑战性的是患有晚期肺病或呼吸高度受损的患者。他们的肿瘤的运动可能是高度不稳定的，具有不均匀的周期和幅度等。本申请中提出的工作的总体目标是改善移动肺肿瘤的放射治疗。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，所述参数在治疗部分期间随时间对其运动进行分类，并且还在治疗过程中累积。个体患者呼吸行为将通过定义运动特性（例如，幅度、频率、速度）以及它们在各种患者生物医学（诸如解剖学和生理学）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。第二个具体目标涵盖肿瘤运动预测模型的开发。在实时放射治疗期间，将建立用于基于先前的运动模式预测肿瘤的未来运动行为的统计模型并进行动态调整。将探索具有加权概率的隐马尔可夫模型。该模型预计将准确预测肿瘤诱导的肿瘤运动，以实现真正的实时IGRT。这项研究是创新的，因为呼吸引起的肿瘤运动尚未得到充分的表征，并且很难预测肺部各部分的肿瘤运动。我们的跨学科研究团队独特地结合了各种数据管理，物理支持和临床专业知识，以达到这项研究的明确结果。公共卫生关系：由于肺癌是美国的头号癌症杀手，五年生存率仅为15%，因此建议对肺癌治疗进行研究具有重要意义。本提案中描述的跨学科和转化研究将导致移动肺肿瘤的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。