权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Improving Cancer Treatment Planning by DMH-Based Inverse Optimization

通过基于 DMH 的逆优化改进癌症治疗计划

基本信息

批准号：
9269157
负责人：
Ivaylo B Mihaylov
金额：
$ 31.85万
依托单位：
UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-07-09 至 2019-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9269157
关键词：
Affect Anatomy Cancer Patient Clinical Clinical Trials Data Data Collection Development Disease Dose Equipment Evaluation Face Generations Goals Head Cancer Head and Neck Squamous Cell Carcinoma Individual Intensity-Modulated Radiotherapy Malignant Neoplasms Malignant neoplasm of lung Malignant neoplasm of prostate Methodology Methods Modality Neck Cancer Non-Small-Cell Lung Carcinoma Normal tissue morphology Organ Outcome Patients Population Probability Process Prostate Radiation Radiation therapy Research Testing Therapeutic Time Time trend Tissues Toxic effect Translations Validation Variant base cancer therapy clinical practice cohort computer framework cost density experience improved innovation interest novel novel therapeutic intervention outcome forecast process optimization prospective pulmonary function standard of care stem treatment planning tumor virtual virtual clinical trial

项目摘要

DESCRIPTION (provided by applicant): Cancer patients continue to represent a challenging disease population, which faces rather poor prognosis with current treatment planning and delivery practices. Venues for a potential dose escalation and/or increased healthy tissue sparing, through innovative therapeutic approaches for those patients, are clearly needed. Current state of the art radiotherapy treatment planning relies on the dose-volume-histogram (DVH) paradigm, where doses to fractional (most often) or absolute volumes of anatomical structures are employed in both optimization and plan evaluation process. It has been argued however, that the effects of delivered dose seem to be more closely related to healthy tissue toxicity (and thereby to clinical outcomes) when dose-mass- histograms (DMHs) are considered in treatment plan evaluation. We propose the incorporation of mass and density information explicitly into the cost functions of the inverse optimization process, thereby shifting from DVH t DMH treatment planning paradigm. This novel DMH-based intensity modulated radiotherapy (IMRT) optimization aims in minimization of radiation doses to a certain mass, rather than a volume, of healthy tissue. Our working hypothesis is that DMH- optimization will reduce doses to healthy tissue substantially. In certain cases, with extensive, difficult to treat disease, lower doses to healthy tissue can be used for isotoxic dose escalation, which may result in an approximately two-fold increase in estimated loco-regional tumor control probability. To test this hypothesis we will pursue the following specific aims: (1) Develop the theoretical and computational framework of the DMH-based IMRT optimization. This framework will incorporate 3D and 4D IMRT as well as 3D volumetric modulated arc (VMAT) planning for different anatomical sites. (2) Investigate different parametric forms for DMH-optimization functions. The ultimate goal would be the simultaneous minimization of healthy tissue doses and/or escalation of therapeutic doses, without violating the established dosimetric tolerances for healthy anatomical structures. And (3) Practical implementation and application of this novel optimization paradigm, where virtual clinical trials for cohorts of lung, head-and-neck, and prostate cancer cases will be performed. Statistical significance of the DMH-optimization dosimetric improvements over standard of care DVH-optimization will be quantified. Prospective 3D and 4D CT data collection will be used to study the interactions between tumor time-trending changes and DMH-based optimization results. 4D CT data will also be used to investigate and quantify the correlation between DMH-based end points and the loss of pulmonary function during and after radiotherapy treatment. The deliverability (with the existing radiotherapy treatment equipment) of our 3D VMAT and 3D/4D IMRT plans will be experimentally verified, thereby paving the road for initiation of clinical trials.

描述（由申请人提供）：癌症患者仍然是一个具有挑战性的疾病人群，他们在当前的治疗计划和实施实践中面临着相当差的预后。显然需要通过针对这些患者的创新治疗方法来增加潜在的剂量和/或增加健康组织的保护。当前最先进的放射治疗计划依赖于剂量体积直方图（DVH）范式，其中在优化和计划评估过程中均采用解剖结构的分数（最常见）或绝对体积的剂量。然而，有人认为，当在治疗计划评估中考虑剂量质量直方图（DMH）时，输送剂量的影响似乎与健康组织毒性（从而与临床结果）更密切相关。我们建议将质量和密度信息明确地纳入逆优化过程的成本函数中，从而从 DVH t DMH 治疗计划范式转变。这种基于 DMH 的新型调强放射治疗 (IMRT) 优化旨在最大限度地减少健康组织一定质量而非一定体积的辐射剂量。我们的工作假设是 DMH 优化将大大减少健康组织的剂量。在某些情况下，对于广泛的、难以治疗的疾病，可以对健康组织使用较低剂量来进行等毒性剂量递增，这可能导致估计的局部肿瘤控制概率增加大约两倍。为了检验这一假设，我们将追求以下具体目标：（1）开发基于 DMH 的 IMRT 优化的理论和计算框架。该框架将结合 3D 和 4D IMRT 以及针对不同解剖部位的 3D 体积调制弧 (VMAT) 规划。 (2) 研究 DMH 优化函数的不同参数形式。最终目标是同时最小化健康组织剂量和/或增加治疗剂量，而不违反健康解剖结构既定的剂量耐受性。 (3) 这种新颖的优化范例的实际实施和应用，其中将对肺癌、头颈癌和前列腺癌病例组进行虚拟临床试验。 DMH 优化剂量测定相对于护理标准 DVH 优化的改善的统计显着性将被量化。前瞻性 3D 和 4D CT 数据收集将用于研究肿瘤时间趋势变化与基于 DMH 的优化结果之间的相互作用。 4D CT 数据还将用于调查和量化基于 DMH 的终点与放疗期间和之后肺功能丧失之间的相关性。我们的3D VMAT和3D/4D IMRT计划的可交付性（使用现有的放射治疗设备）将通过实验验证，从而为临床试验的启动铺平道路。