4D Robust Optimization in Intensity-Modulated Proton Therapy

调强质子治疗中的 4D 鲁棒优化

基本信息

批准号：
8906504
负责人：
Wei Liu
金额：
$ 9.99万
依托单位：
MAYO CLINIC ARIZONA
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：
2012-08-13 至 2017-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8906504
关键词：
Accounting Address Algorithms Anatomy Archives Area Attention Award Biometry Body Weight decreased Caliber Cancer Center Cancer Hospital Cancer Patient Characteristics Clinical Clinical Research Code Computers Development Development Plans Distal Doctor of Philosophy Dose Drops Ensure Environment Equation Faculty Four-dimensional Future Goals Grant Health Sciences High Performance Computing Individual Institution Intensity modulated proton therapy Intensity-Modulated Radiotherapy International Investigation Joints Knowledge Laboratories Lateral Lead Learning Malignant Neoplasms Malignant neoplasm of abdomen Malignant neoplasm of liver Malignant neoplasm of lung Malignant neoplasm of thorax Maps Measurable Medical Imaging Medical Radiation Physics Memory Mentors Methodology Methods Modality Modeling Motion Normal tissue morphology Organ Patients Phase Photons Physics Probability Proton Radiation Protons Quantum Mechanics Radiation Radiation Physics Radiation therapy Radiobiology Research Research Personnel Residual state Resources Respiration Rice Risk Safety Scanning Scheme Side Solid Source Spottings Testing Texas Therapeutic Therapeutic Studies Time Tissues Training Training Programs Translational Research Uncertainty United States Universities University of Texas M D Anderson Cancer Center Validation Variant Work X-Ray Computed Tomography base cancer radiation therapy cancer therapy career career development computer design design distributed memory dosimetry experience image registration improved innovation mathematical algorithm novel patient population perturbation theory professor proton beam proton therapy respiratory skills software development symposium treatment center treatment planning tumor

项目摘要

DESCRIPTION (provided by applicant): The applicant's immediate career goal is to make the transition to a high-caliber independent researcher and to establish a small laboratory in an intense and supportive research environment. In the long term, the applicant hopes to develop a solid academic career focusing on translational research in the area of medical radiation physics that impacts various aspects of the state-of-the-art radiotherapy modalities. The candidate, who obtained his PhD from Princeton University in 2007 and later worked at the Los Alamos National Laboratory as a postdoctoral researcher, has extensive prior experience in computational physics, mathematics and algorithms, and software development, especially code development in massive parallel high-performance computing (HPC). The candidate also has solid analytical and mathematical skills to solve complicated physics problems, along with an interdisciplinary background. In July 2010, the candidate joined the faculty of the Department of Radiation Physics of The University of Texas MD Anderson Cancer Center as an assistant professor (research track). MD Anderson is a research-driven, comprehensive cancer hospital and is the leading cancer center in the United States. The Department of Radiation Physics provides the clinical, research, and educational resources necessary to support physics and dosimetry research related to cancer therapy. The department's Proton Therapy Center-Houston (PTC-H) began patient treatments in May 2006. PTC-H is one of few proton treatment centers in the world to have the capability to deliver intensity- and energy-modulated treatments with scanned proton beams, which is the major focus of the proposed research. During the award period, the candidate will focus on the development and validation of novel advanced radiation therapy methodologies. With the support of this K25 grant, the applicant plans to 1) obtain in-depth knowledge and hands-on experience in the clinical and research aspects of radiation therapy; 2) obtain in- depth knowledge of anatomy; 3) obtain in-depth knowledge of medical imaging; 4) obtain moderate knowledge of biostatistics; and 5) obtain moderate knowledge of radiobiology. To achieve these objectives, the applicant will work with a group of experienced mentors and collaborating researchers on joint projects focusing on the radiotherapy of cancers, take academic courses at Rice University and The University of Texas-Health Science Center at Houston, undergo clinical training in radiation therapy, and attend seminars and conferences. Four-dimensional (4D) robust optimization of intensity-modulated proton therapy (IMPT) has been chosen as the research topic for this K25 training program to help the applicant gain the experience necessary to become an independent investigator. The use of IMPT to treat lung cancers presents numerous challenges that need to be addressed through research to maximize the therapeutic benefits of this promising modality. What the candidate learns during this research will be widely applicable to many areas of research in this field. In principle, IMPT has the greatest potential to provide highly conformal tumor target coverage, while sparing adjacent healthy organs. However, characteristics of protons (e.g., the abrupt drop-off of dose beyond the range and scattering) make IMPT highly vulnerable to uncertainties. Sources of uncertainty include tumor shrinkage, weight loss, variation in patient setup, respiratory motion, uncertainties in CT numbers and stopping power ratios, and approximations in proton dose calculation algorithms. The current practice for managing uncertainties in IMPT is similar to that for intensity-modulated radiation therapy (IMRT), i.e., assigning a safety margin around the clinical target volume to produce a planning target volume. The resulting dose distributions are, in general, not robust in the face of uncertainties, i.e., what is delivered to the patient may be significantly different from what is seen on the computer-designed treatment plan and may lead to unforeseen clinical consequences. Therefore, investigations leading to the development of suitable 4D robust optimization methods to improve the optimality and robustness of IMPT plans to uncertainties, including regular and irregular motion, are vital. Our hypothesis is that 4D robust optimization can render IMPT plans less sensitive to uncertainties and achieve better sparing of normal tissues (both by at least a factor of two) than conventional plans optimized on the basis of margins. We propose to test our hypothesis in the following specific aims: (1) to quantify anatomy motion and its uncertainty; (2) to develop and implement 4D IMPT optimization; (3) to enhance the IMPT plan robustness; and (4) to validate IMPT 4D robust optimization. Compared to previous 4D robust optimization approaches in IMRT, the research proposed by the applicant has several innovative aspects, including a "customized", as-small-as-necessary margin optimized spontaneously to handle uncertainties and regular motion, the use of perturbation theory, widely used in quantum mechanics to solve the Schr¿dinger equation, to handle irregular motion, and memory-distributed parallelization to solve the challenging high-computer-memory requirement problem. We expect that our pioneering 4D robust optimization research in IMPT will fill gaps in our knowledge about appropriate ways to minimize the influence of uncertainties in IMPT and lead to significant benefits for cancer patients, especially those with thoracic and abdominal cancers. This project doesn't involve activities outside of the United States or partnerships with international collaborators.

描述（由申请人证明）：申请人的职业目标是向高素质独立性的脚趾过渡到在强烈和支持性的研究环境中，申请人希望发展扎实专注于影响最先进的其他疗法方式的医学辐射物理学领域，候选人于2007年从普林斯顿大学获得博士学位研究人员在计算物理学，数学和算法和软件开发方面具有丰富的先前经验，在大规模平行的高性能计算（HPC）中，尤其是代码开发（HPC）。背景。为了支持与癌症治疗有关的物理学和剂量学研究候选人将重点放在新的晚期放射治疗方法上，并在该T的支持下，申请人计划1）获得了深入的知识和动手体验）获得对医学成像的深入了解；休斯敦的Iversity和Texas-Health Scienter（4D）对强度模块化质子疗法（IMPT）的强大优化被选为该主题，以帮助申请人获得必要的经验使用IMPT来信任肺癌提出了许多挑战，这些挑战通过在研究期间最大化这种有希望的方式的治疗效果。最大的是提供高度的保形肿瘤目标覆盖范围，同时保留健康的器官。设置，CT数字的不确定性和质子剂量计算算法中的功率比率。，面对不确定性，即交付给患者的东西可能与计算机设计的顶部到不可预见的临床奉献的情况有显着差异。我们的假设是定期的，我们的假设是，强大的优化可以使敏感的计划较低，而不是根据优化利率优化的传统计划来实现MMAL ES的更好的保留。我们在以下特定目标中的假设：（1）量化解剖学的运动，并且是不可能的；严重性创新的方面自定义”，“依靠的边缘”优化自发性来处理不确定性和常规运动，使用扰动理论，在量子力学中广泛用于求解Schr¿ Dinger方程，处理不规则的运动，并将记忆分布的并行化与具有挑战性的高计算机记忆EM。癌症患者在美国以外的活动或与国际合作者的合作伙伴关系。

项目成果

期刊论文数量（37）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Exploratory study of the association of volumetric modulated arc therapy (VMAT) plan robustness with local failure in head and neck cancer.

DOI：
10.1002/acm2.12099
发表时间：
2017-07
期刊：
Journal of applied clinical medical physics
影响因子：
2.1
作者：
Liu W;Patel SH;Harrington DP;Hu Y;Ding X;Shen J;Halyard MY;Schild SE;Wong WW;Ezzell GE;Bues M
通讯作者：
Bues M

Comparison of linear and nonlinear programming approaches for "worst case dose" and "minmax" robust optimization of intensity-modulated proton therapy dose distributions.

强度调制质子治疗剂量分布的“最坏情况剂量”和“最小最大”稳健优化的线性和非线性编程方法的比较。

DOI：
10.1002/acm2.12033
发表时间：
2017
期刊：
Journal of applied clinical medical physics
影响因子：
2.1
作者：
Zaghian,Maryam;Cao,Wenhua;Liu,Wei;Kardar,Laleh;Randeniya,Sharmalee;Mohan,Radhe;Lim,Gino
通讯作者：
Lim,Gino

A novel and individualized robust optimization method using normalized dose interval volume constraints (NDIVC) for intensity-modulated proton radiotherapy.

一种新颖的个性化鲁棒优化方法，使用归一化剂量间隔体积约束（NDIVC）进行调强质子放射治疗。

DOI：
10.1002/mp.13276
发表时间：
2019
期刊：
Medical physics
影响因子：
3.8
作者：
Shan,Jie;Sio,TerenceT;Liu,Chenbin;Schild,StevenE;Bues,Martin;Liu,Wei
通讯作者：
Liu,Wei

Technical Note: Multiple energy extraction techniques for synchrotron-based proton delivery systems may exacerbate motion interplay effects in lung cancer treatments.

DOI：
10.1002/mp.15056
发表时间：
2021-09
期刊：
Medical physics
影响因子：
3.8
作者：
Younkin JE;Morales DH;Shen J;Ding X;Stoker JB;Yu NY;Sio TT;Daniels TB;Bues M;Fatyga M;Schild SE;Liu W
通讯作者：
Liu W

Mixed integer programming with dose-volume constraints in intensity-modulated proton therapy.

调强质子治疗中具有剂量体积限制的混合整数规划。