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: 8438590
• 负责人: Geoffrey D Hugo
• 金额: $ 32.67万
• 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/8438590
• 关键词: Adverse effects; 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; 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（由申请人提供）：安全地将辐射剂量升级为局部增强的非小细胞肺癌患者，以改善局部肿瘤控制和存活，这是由于这些患者的多种类型和大量的日常几何变化，这仍然是一个挑战。可变形的图像注册（DIR）已成为目标和风险结构日常映射的强大工具，并评估累积剂量以指导治疗计划的适应性，以减轻几何变化。然而，在治疗过程中，组织和器官特征的变化 - 组织形成，健康与患病之间的组织状况的变化违反了当前DIR算法所基于的基本原理。由于原发性肿瘤和淋巴结质量的变化，在肺癌中，组织特征的变化在响应治疗和形成，分辨率或部分崩溃的肺，胸膜液和其他相关病理学的响应时很常见。组织特征的变化是最容易受到输送错误的几何变化类型，最有可能受益于计划适应，也是当前DIR算法的最常见位点。因此，DIR的失败将误差引入了自适应放射疗法过程中，从而限制了安全升级辐射剂量的能力。 我们建议开发和评估一种新型的DIR算法，旨在在肿瘤和肺组织特征变化的困难条件下准确执行。该算法是专门设计的，旨在鉴定和注册解剖结构的形状和组织特征一致的区域，同时忽略了注册中可变特征区域（例如倒塌的肺和肿瘤），并使用邻接的测量型在病理区域中重构病理区域中的变形，该区域是一致的。该项目将以三个具体目标完成。在特定的目标1中，将开发和验证一致的解剖学登记，并验证呼吸的屏息图像和自由呼吸的肺癌患者的数据库。在特定的目标2中，我们将扩展一致的解剖学登记到锥形束CT图像，以便快速估算组织位置并与患者在治疗位置进行剂量。在特定的目标3中，将测量DIR误差对准确适应治疗计划的能力的影响，以进行一致的解剖学注册和常规DIR，以进行各种形式的适应性放射疗法。该项目将为放射疗法社区提供准确的DIR，以支持适应辐射疗法治疗计划，以减轻局部不小型细胞肺癌的几何变化。