Dynamic multi-organ anatomical models for hypofractionated RT design and delivery

用于大分割放疗设计和实施的动态多器官解剖模型

• 批准号: 7771627
• 负责人: Kristy Brock
• 金额: $ 21.03万
• 依托单位: UNIVERSITY HEALTH NETWORK
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-03-25 至 2012-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7771627
• 关键词: Abdomen; Accounting; Anatomic Models; Anatomy; Biomechanics; Breathing; Chest; Clinical; Clinical Research; Clinical Trials; Complication; Development; Diagnostic Imaging; Documentation; Dose; Elements; Ensure; Environment; Evaluation; Exhalation; Fractionation; Future; Goals; Hand; Hospitals; Image; Imaging Techniques; Imaging technology; Institution; Investigation; Lead; Linear Models; Liver; Lung; Malignant neoplasm of liver; Methods; Modality; Modeling; Morbidity - disease rate; Motion; Nature; Normal tissue morphology; Organ; Outcome; Pancreas; Patients; Physiological; Positioning Attribute; Positron-Emission Tomography; Process; Process Assessment; Property; Protocols documentation; Radiation therapy; Reporting; Research; Research Infrastructure; Scheme; Scientist; Site; Slice; Solutions; Staging; System; Techniques; Technology; Testing; Time; Toxic effect; Translational Research; Translations; Uncertainty; Work; base; cancer therapy; clinical practice; clinically significant; cone-beam computed tomography; design; early experience; follow-up; human tissue; image registration; imaging modality; improved; innovation; novel; programs; response; simulation; single photon emission computed tomography; tool; treatment planning; treatment response; tumor

DESCRIPTION (provided by applicant): Advances in hypofractionated radiotherapy techniques have shown promise in the treatment of cancers that are conventionally associated with high morbidity and poor local control (e.g. lung and liver cancer). The small number of high dose treatment fractions requires superior precision and accuracy in target delineation, conformal treatment planning, and target localization at the time of treatment. Advances in imaging for target identification, volumetric imaging capabilities at treatment, and temporal imaging technologies, increase the capability of identifying the tumor during simulation, planning, and delivery. The spatial registration of this information, which is critical to correlate the unique information from each image, is limited by the lacking ability to integrate all available information into one comprehensive model of the patient. Early experience with dynamic multi-organ anatomical models for deformable registration has lead to the hypothesis that deformation technologies will improve the quality of treatment and lead to clinically significant improvements in tumor control and reduced toxicity. While testing this hypothesis will require a comprehensive program of multi-institution clinical trials, these methods need to be established and evaluated prior to deployment in clinical studies. This proposal sets out three specific aims to assure that the technologies are ready for translation into the clinical context, specifically in the lung, liver, and pancreas. In specific aim 1, dynamic multi-organ anatomical models will be developed and validated for the lung, liver, and pancreas. The accuracy of these models and linear interpolation between breathing states will be quantified. Heterogeneous material models will be optimized for the lung and liver. The influence of multi-organ deformable registration on the design and targeting of hypofractionated radiotherapy will be investigated in specific aim 2. The increase in accuracy of multi-modality treatment planning with deformable registration will be evaluated. The improvements in dosimetric accuracy with the inclusion of motion and deformation due to breathing will be quantified. The translation of this increase in accuracy into clinical dose effect models will be investigated. Specific aim 3 evaluates the impact of deformable registration on documentation and accounting of dose in hypofractionated radiotherapy. The improvements in accuracy of image guidance using deformable registration will be assessed. The increase in accuracy of the documentation of accumulated dose over treatment will be investigated, as well as the translation of this improvement in dose effect models. The goal of this research is to improve the accuracy and reduce the uncertainty in radiation therapy. Through the use of dynamic multi-organ anatomical models the wealth of information obtained from advanced imaging techniques will be combined into one, clear, model of the patient. This enhanced patient model will allow improved accuracy in the design and implementation of treatment.

描述（由申请人提供）：大分割放射治疗技术的进步已显示出治疗通常与高发病率和局部控制不良相关的癌症（例如肺癌和肝癌）的前景。少量的高剂量治疗分次需要在治疗时靶区描绘、适形治疗计划和靶区定位方面具有极高的精度和准确度。用于目标识别的成像、治疗时的体积成像能力和时间成像技术的进步，提高了在模拟、计划和交付过程中识别肿瘤的能力。这一信息的空间配准对于关联每张图像的独特信息至关重要，但由于缺乏将所有可用信息整合到患者的一个综合模型中的能力而受到限制。用于变形配准的动态多器官解剖模型的早期经验得出这样的假设：变形技术将提高治疗质量，并导致肿瘤控制和毒性降低的临床显着改善。虽然检验这一假设需要一个全面的多机构临床试验计划，但这些方法需要在临床研究中部署之前建立和评估。该提案提出了三个具体目标，以确保这些技术准备好转化为临床环境，特别是肺、肝脏和胰腺。在具体目标 1 中，将为肺、肝脏和胰腺开发并验证动态多器官解剖模型。这些模型的准确性和呼吸状态之间的线性插值将被量化。异质材料模型将针对肺和肝脏进行优化。将在具体目标2中研究多器官变形配准对大分割放射治疗的设计和靶向的影响。将评估使用变形配准的多模态治疗计划的准确性的提高。由于呼吸引起的运动和变形，剂量测定精度的提高将被量化。将研究将这种准确性的提高转化为临床剂量效应模型。具体目标 3 评估变形配准对大分割放射治疗中剂量记录和计算的影响。将评估使用变形配准的图像引导准确性的改进。将研究治疗期间累积剂量记录的准确性的提高，以及剂量效应模型中这种改进的转化。这项研究的目标是提高放射治疗的准确性并减少不确定性。通过使用动态多器官解剖模型，从先进成像技术获得的大量信息将被整合到一个清晰的患者模型中。这种增强的患者模型将提高治疗设计和实施的准确性。