Consistent anatomy registration for lung cancer adaptive radiation therapy

肺癌适应性放射治疗的一致解剖配准

• 批准号: 8609007
• 负责人: Geoffrey D Hugo
• 金额: $ 29.73万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8609007
• 关键词: Adverse effects; Algorithm Design; Algorithms; Anatomy; Architecture; Back; Breathing; Cancer Patient; Clinical; Clinical Trials; Communities; Complex; Computer software; Databases; Development; Diagnostic; Dose; Exhibits; Failure; Feedback; Goals; Gold; Image; Immunoglobulin Variable Region; Knowledge; Link; Liquid substance; Location; Lung; Lung Neoplasms; Lymph Node Mass; Malignant neoplasm of lung; Manuals; Maps; Measures; Methods; Modeling; Morphologic artifacts; Motion; Non-Small-Cell Lung Carcinoma; Normal tissue morphology; Organ; Outcome; Pathology; Patients; Pleural; Positioning Attribute; Primary Neoplasm; Process; Radiation; Radiation therapy; Recovery; Relative (related person); Research; Research Infrastructure; Resolution; Respiration; Risk; Series; Shapes; Site; Structure; Structure of parenchyma of lung; System; Testing; Time; Tissues; Tumor Tissue; Validation; X-Ray Computed Tomography; base; cancer radiation therapy; cone-beam computed tomography; design; image guided radiation therapy; image processing; image registration; imaging modality; improved; open source; public health relevance; reconstruction; response; tool; treatment planning; treatment strategy; tumor; virtual

DESCRIPTION (provided by applicant): Safely escalating the radiation dose to locally-advanced non-small cell lung cancer patients to improve local tumor control and survival remains a challenge due to the numerous types and large magnitude of day-to-day geometric change in these patients. Deformable image registration (DIR) has emerged as a powerful tool for day-to-day mapping of target and risk structures and assessing the cumulative delivered dose to guide adaptation of the treatment plan to mitigate geometric changes. However, change in tissue and organ character - tissue formation, disintegration, and change of tissue condition between healthy and diseased - during the treatment course violates the fundamental principles upon which current DIR algorithms are based. Tissue character changes are common in lung cancer, due to primary tumor and involved lymph node mass changes in response to therapy and formation, resolution, or progression of partially collapsed lung, pleural fluid, and other associated pathologies. Tissue character changes are the type of geometric change most susceptible to delivery errors, the most likely to benefit from plan adaptation, and the most common site of failure of current DIR algorithms. Consequently, failure of DIR introduces error into the adaptive radiotherapy process, limiting the ability to safely escalate the radiation dose. We propose to develop and evaluate a new type of DIR algorithm designed to perform accurately under the difficult conditions of tumor and lung tissue character change. The algorithm is specifically designed to identify and register regions of anatomy consistent in shape and tissue character from day to day, while ignoring regions of variable character (such as collapsed lung and tumor) in the registration, and reconstructing deformation in the pathological regions using the measured deformation of the adjacent, consistent anatomical features. The project will be accomplished in three specific aims. In specific aim 1, consistent anatomy registration will be developed and validated against a database of fan beam CT images of breath hold and free-breathing lung cancer patients undergoing image- guided radiation therapy. In specific aim 2, we will extend consistent anatomy registration to cone beam CT images to enable rapid estimation of tissue location and delivered dose with the patient in the treatment position. In specific aim 3, the impact of DIR error on the ability to accurately adapt the treatment plan will be measured for consistent anatomy registration and for conventional DIR, for a variety of forms of adaptive radiotherapy. This project will provide the radiation therapy community with accurate DIR to support adapting the radiation therapy treatment plan to geometric change in locally-advanced non-small cell lung cancer.

描述（由申请方提供）：由于局部晚期非小细胞肺癌患者的日常几何变化类型众多且幅度较大，因此安全地递增局部晚期非小细胞肺癌患者的辐射剂量以改善局部肿瘤控制和生存仍然是一项挑战。可变形图像配准（Deformable Image Registration，简称DAI）已成为一种强大的工具，可用于目标和风险结构的日常映射，并评估累积输送剂量，以指导治疗计划的调整，从而减轻几何变化。然而，在治疗过程期间，组织和器官特性的变化-组织形成、分解以及健康和患病之间的组织状况的变化-违反了当前的生物识别算法所基于的基本原理。组织特征变化在肺癌中很常见，这是由于原发性肿瘤和受累淋巴结肿块对治疗的反应以及部分塌陷肺、胸膜液和其他相关病理的形成、消退或进展。组织特征变化是最易受输送错误影响的几何变化类型，最有可能受益于计划调整，并且是当前并行计算算法最常见的失败部位。因此，放射治疗的失败将误差引入到自适应放射治疗过程中，限制了安全地逐步增加辐射剂量的能力。 我们建议开发和评估一种新型的并行算法，其设计用于在肿瘤和肺组织特征变化的困难条件下准确地执行。该算法专门设计用于识别和配准每天形状和组织特征一致的解剖区域，同时忽略配准中的可变特征区域（例如塌陷的肺和肿瘤），并使用相邻的一致解剖特征的测量变形重建病理区域中的变形。该项目将实现三个具体目标。在特定目标1中，将开发一致的解剖配准，并根据接受图像引导放射治疗的屏气和自由呼吸肺癌患者的扇形束CT图像数据库进行验证。在具体目标2中，我们将一致的解剖配准扩展到锥形束CT图像，以便能够快速估计组织位置和患者处于治疗位置时的输送剂量。在特定目标3中，将测量一致解剖配准和常规放射治疗以及各种形式的自适应放射治疗的放射治疗误差对准确调整治疗计划能力的影响。该项目将为放射治疗界提供准确的数据，以支持放射治疗计划适应局部晚期非小细胞肺癌的几何变化。