Consistent anatomy registration for lung cancer adaptive radiation therapy

描述(申请人提供)：由于局部晚期非小细胞肺癌患者存在多种类型和大量的日常几何变化，因此安全地增加对这些患者的放射剂量以改善局部肿瘤控制和存活率仍然是一项挑战。可变形图像配准(DIR)已成为一种强大的工具，可用于日常绘制靶点和风险结构图，并评估累积递送剂量，以指导治疗计划的适应，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、分解以及健康和疾病之间组织状况的变化-违反了当前DIR算法所基于的基本原则。组织特征改变在肺癌中很常见，这是由于原发肿瘤和累及的淋巴结肿块对治疗的反应以及部分塌陷的肺、胸腔积液和其他相关病理的形成、消退或进展。组织特征变化是最容易受到传递错误影响的几何变化类型，也是最有可能受益于计划调整的类型，也是当前DIR算法最常见的失败部位。因此，DIR的失败在适应性放射治疗过程中引入了错误，限制了安全地增加放射剂量的能力。 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。该算法专门用于识别和配准每天在形状和组织特征上一致的解剖区域，而在配准中忽略可变特征的区域(如塌陷的肺和肿瘤)，并使用测量的相邻一致的解剖特征的变形来重建病理区域中的变形。该项目将以三个具体目标完成。在具体目标1中，将开发一致的解剖配准，并对照接受图像引导放射治疗的屏气和自由呼吸肺癌患者的扇束CT图像数据库进行验证。在具体目标2中，我们将把一致的解剖配准扩展到锥束CT图像，以便能够在患者处于治疗位置时快速估计组织位置和输送剂量。在具体目标3中，将测量DIR误差对准确调整治疗计划的能力的影响，以进行一致的解剖配准和常规DIR，以及各种形式的适应性放射治疗。该项目将为放射治疗界提供准确的DIR，以支持使放射治疗计划适应局部晚期非小细胞肺癌的几何变化。

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.