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

描述(由申请人提供)：低分割放射治疗技术的进步在治疗传统上与高发病率和局部控制不良有关的癌症(如肺癌和肝癌)方面显示出希望。少量的大剂量治疗部分要求在治疗时靶点描绘、适形治疗计划和靶点定位方面具有较高的精密度和准确性。用于靶点识别的成像技术、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、规划和交付过程中识别肿瘤的能力。这种信息的空间配准对于关联来自每个图像的唯一信息是关键的，但由于缺乏将所有可用信息集成到患者的一个综合模型中的能力而受到限制。早期用于可变形配准的动态多器官解剖模型的经验导致了这样的假设，即变形技术将提高治疗质量，并在临床上显著改善肿瘤控制并降低毒性。虽然验证这一假设需要一个多机构临床试验的综合计划，但在临床研究中部署之前，这些方法需要被建立和评估。这项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背景，特别是在肺、肝脏和胰腺。在具体目标1中，将开发和验证肺、肝和胰腺的动态多器官解剖模型。这些模型的精确度和呼吸状态之间的线性内插将被量化。肺和肝脏的异质材料模型将得到优化。在特定的目标2中，将研究多器官可变形配准对低分割放射治疗设计和靶向的影响。评估具有可变形配准的多模式治疗计划的准确性的提高。将呼吸引起的运动和变形纳入剂量测量精度的改进将被量化。这种准确性的提高将被研究转化为临床剂量效应模型。具体目的3评估可变形登记对记录和核算低分割放射治疗剂量的影响。将评估使用可变形配准在图像制导精度方面的改进。将调查累积剂量超过治疗记录的准确性的增加，以及剂量效应模型中这一改进的翻译。本研究的目的是提高放射治疗的准确性，减少放射治疗的不确定性。通过使用动态多器官解剖模型，从先进的成像技术获得的丰富信息将被组合成一个清晰的患者模型。这一增强的患者模型将提高治疗设计和实施的准确性。

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.