Consistent anatomy registration for lung cancer adaptive radiation therapy

描述（由申请人提供）：由于局部晚期非小细胞肺癌患者类型繁多，日常几何变化幅度大，因此安全增加局部晚期非小细胞肺癌患者的放射剂量以改善局部肿瘤控制和生存率仍然是一个挑战。可变形图像配准（DIR）已成为一种强大的工具，可用于绘制目标和风险结构的日常地图，并评估累积剂量，以指导治疗计划的调整，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、解体和健康与患病之间组织状态的变化——违背了当前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受累淋巴结肿块对治疗的反应以及部分肺萎陷、胸腔积液和其他相关病理的形成、消退或进展。组织特征变化是最易受传递错误影响的几何变化类型，最可能受益于计划适应，也是当前DIR算法最常见的失败部位。因此，DIR的失败给自适应放疗过程引入了错误，限制了安全提高辐射剂量的能力。

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.