Comprehensive analysis and mitigation of the clinical effects of delineation and geometric uncertainties in conformal radiation therapy

适形放射治疗中轮廓和几何不确定性临床效果的综合分析和缓解

• 批准号: 10218076
• 负责人: JEFFREY Vincent SIEBERS
• 金额: $ 36.94万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10218076
• 关键词: Algorithms; Budgets; Characteristics; Clinic; Clinical; Clinical Trials; Complication; Computers; Computing Methodologies; Conformal Radiotherapy; Data; Dependence; Dose; Ensure; Functional Imaging; Head and Neck Cancer; Human; Image; Joints; Knowledge; Light; Manuals; Methods; Modeling; Normal tissue morphology; Organ; Outcome; Patients; Physicians; Positron-Emission Tomography; Prevention strategy; Probability; Process; Quality Control; Radiation; Radiation therapy; Research Personnel; Residual state; Retreatment; Risk; Role; Structure; System; Therapeutic; Tissues; Treatment Efficacy; Treatment Failure; Treatment outcome; Tumor Tissue; Uncertainty; Variant; alternative treatment; base; clinical effect; clinically relevant; design; image registration; imaging modality; improved; individual patient; interest; knowledge base; pancreatic cancer patients; patient variability; prevent; radiation delivery; therapy design; treatment planning; treatment risk; tumor

Project Summary / Abstract Advances in radiotherapy (RT) delivery seek to improve dose conformity to increase the therapeutic ratio. A key step in this process is the delineation of treatment targets and normal tissue avoidance regions. As treatment margins around delineated volumes shrink, if delineation variability is not accounted for, disparate volumes identified by different delineators, different imaging modalities, and different structure mapping algorithms may dominate the efficacy of both an individual patients' treatment and the collective results of clinical trials. While efficacy should be enhanced with improved delineation consistency, improved baseline imaging (including functional imaging) to more clearly see the tissues, and improved deformable registration algorithms to better align delineations from different images, benefits from such improvements will be jeopardized until methods to quantify and communicate the clinical effects of the patient-specific residual uncertainties in these processes exist. Uncertainty quantification enables mitigation as well as determination of when further uncertainty reductions provide no benefit. This project aims to (1) identify clinical situations in which delineation variability negatively impacts treatment efficacy in light of other inherent treatment uncertainties, (2) model delineation variability so that it may be estimated on a per-patient basis and communicated to the treatment team, and (3) develop treatment planning and delivery methods that mitigate the joint effect of delineation and other uncertainties in radiation therapy, thereby creating efficacious radiation therapy treatment plans.

项目摘要 /摘要 放射疗法（RT）的进展寻求提高剂量一致性以提高治疗比率。一个 此过程的关键步骤是描述治疗靶标和正常组织回避区域。作为 如果未考虑描述可变性，则围绕划定的体积缩小的治疗余量缩小，则 由不同的描述器，不同的成像方式和不同的结构映射确定的卷 算法可能主导单个患者治疗的疗效和集体结果 临床试验。虽然应通过提高的描述一致性来增强功效，但基准的提高 成像（包括功能成像）更清楚地看到组织，并改善了可变形的注册 算法可以更好地调整不同图像的描述，从而将这些改进带来的好处是 危害到量化和传达患者特异性残留的临床作用的方法 这些过程中存在不确定性。不确定性量化可以缓解和确定 当进一步的不确定性降低没有任何好处时。该项目的目的是（1）确定 鉴于其他固有的治疗 不确定性，（2）模型描述可变性，以便以每个患者的估计和 与治疗团队交流，以及（3）制定减轻治疗计划和交付方法 辐射疗法中描述和其他不确定性的联合作用，从而产生有效的辐射 治疗计划。