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: 10460951
• 负责人: JEFFREY Vincent SIEBERS
• 金额: $ 13.79万
• 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/10460951
• 关键词: Algorithms; Budgets; Characteristics; Clinic; Clinical; Clinical Trials; Communication; Complication; Computers; Computing Methodologies; Conformal Radiotherapy; Data; Dependence; Disparate; Dose; Ensure; Functional Imaging; Head and Neck Cancer; Human; Image; Joints; Knowledge; Light; Manuals; Maps; Methods; Modeling; Normal tissue morphology; Organ; Outcome; PET/CT scan; Patients; Physicians; 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; 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; usability

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 放射治疗(RT)的进展寻求改善剂量顺应性，以提高治疗率。一个 这一过程的关键一步是划定治疗靶点和正常组织回避区域。AS 如果不考虑描绘的可变性，围绕描绘的体积的治疗范围缩小，是不同的 由不同的描述者、不同的成像方式和不同的结构映射确定的体积 算法可能会主导单个患者的治疗效果和 临床试验。虽然疗效应该通过改善划定一致性来增强，但改善的基线 成像(包括功能成像)，以更清楚地看到组织，并改进可变形配准 更好地对齐不同图像的轮廓的算法，这些改进的好处将是 直到量化和传达患者特定残留物的临床效果的方法为止 这些过程中存在不确定性。不确定性量化既可以缓解，也可以确定 什么时候进一步减少不确定性不会带来任何好处。这个项目的目的是(1)确定临床情况 根据其他固有的治疗方法，哪些描述的可变性会对治疗效果产生负面影响 不确定性，(2)模型描述的可变性，以便可以在每个患者的基础上进行估计 与治疗团队沟通，以及(3)制定治疗计划和提供方法，以缓解 放射治疗中的描绘和其他不确定性的共同作用，从而产生有效的辐射 治疗方案。

项目成果

Dose escalation in the definite target volume.

确定目标体积的剂量升级。

• DOI: 10.1002/mp.14164
• 发表时间: 2020-07
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Watkins WT;Nourzadeh H;Siebers JV
• 通讯作者: Siebers JV

OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines.

• DOI: 10.1088/1361-6560/ac8044
• 发表时间: 2022-09-12
• 期刊: Physics in medicine and biology
• 影响因子: 3.5
• 作者: Babier A;Mahmood R;Zhang B;Alves VGL;Barragán-Montero AM;Beaudry J;Cardenas CE;Chang Y;Chen Z;Chun J;Diaz K;David Eraso H;Faustmann E;Gaj S;Gay S;Gronberg M;Guo B;He J;Heilemann G;Hira S;Huang Y;Ji F;Jiang D;Carlo Jimenez Giraldo J;Lee H;Lian J;Liu S;Liu KC;Marrugo J;Miki K;Nakamura K;Netherton T;Nguyen D;Nourzadeh H;Osman AFI;Peng Z;Darío Quinto Muñoz J;Ramsl C;Joo Rhee D;David Rodriguez J;Shan H;Siebers JV;Soomro MH;Sun K;Usuga Hoyos A;Valderrama C;Verbeek R;Wang E;Willems S;Wu Q;Xu X;Yang S;Yuan L;Zhu S;Zimmermann L;Moore KL;Purdie TG;McNiven AL;Chan TCY
• 通讯作者: Chan TCY

An Automated Workflow to Improve Efficiency in Radiation Therapy Treatment Planning by Prioritizing Organs at Risk.

• DOI: 10.1016/j.adro.2020.06.012
• 发表时间: 2020-11
• 期刊: Advances in radiation oncology
• 影响因子: 2.3
• 作者: Aliotta E;Nourzadeh H;Choi W;Leandro Alves VG;Siebers JV
• 通讯作者: Siebers JV