4D Robust Optimization in Intensity-Modulated Proton Therapy

描述（由申请人提供）：申请人的直接职业目标是过渡到一个高素质的独立研究人员，并建立一个小实验室在激烈和支持的研究环境。从长远来看，申请人希望发展一个坚实的学术生涯，专注于医学辐射物理领域的转化研究，影响最先进的放射治疗方式的各个方面。该候选人于2007年在普林斯顿大学获得博士学位，后来在洛斯阿拉莫斯国家实验室担任博士后研究员，在计算物理，数学和算法以及软件开发方面拥有丰富的经验，特别是大规模并行高性能计算（HPC）的代码开发。候选人还具有扎实的分析和数学技能，以解决复杂的物理问题，沿着跨学科背景。2010年7月，候选人加入了德克萨斯大学MD安德森癌症中心放射物理系的教员，担任助理教授（研究轨道）。MD安德森是一家以研究为导向的综合性癌症医院，是美国领先的癌症中心。放射物理系提供必要的临床，研究和教育资源，以支持与癌症治疗相关的物理学和剂量学研究。该部门的质子治疗中心-休斯顿（PTC-H）于2006年5月开始治疗患者。PTC-H是世界上为数不多的质子治疗中心之一，能够通过扫描质子束提供强度和能量调制治疗，这是拟议研究的主要重点。在获奖期间，候选人将专注于开发和验证新的先进放射治疗方法。在K25资助的支持下，申请人计划1）获得放射治疗临床和研究方面的深入知识和实践经验; 2）获得解剖学的深入知识; 3）获得医学成像的深入知识; 4）获得生物统计学的中等知识; 5）获得放射生物学的中等知识。为了实现这些目标，申请人将与一群经验丰富的导师和合作研究人员合作，共同开展癌症放射治疗的联合项目，在赖斯大学和德克萨斯大学休斯顿健康科学中心学习学术课程，接受放射治疗的临床培训，并参加研讨会和会议。强度调制质子治疗（IMPT）的四维（4D）稳健优化已被选为该K25培训计划的研究主题，以帮助申请人获得成为独立研究者所需的经验。使用IMPT治疗肺癌提出了许多挑战，需要通过研究来解决这些挑战，以最大限度地提高这种有前途的方式的治疗益处。候选人在这项研究中学到的东西将广泛适用于该领域的许多研究领域。原则上，IMPT 具有提供高度适形的肿瘤靶覆盖的最大潜力，同时保留邻近的健康器官。然而，质子的特性（例如，超出范围的剂量突然下降和散射）使得IMPT非常容易受到不确定性的影响。不确定性的来源包括肿瘤缩小、体重减轻、患者设置的变化、呼吸运动、CT数量和停止功率比的不确定性以及质子剂量计算算法的近似值。目前管理IMPT中不确定性的做法与调强放射治疗（IMRT）的做法类似，即，在所述临床目标体积周围分配安全裕度以产生计划目标体积。一般而言，所得到的剂量分布在不确定性面前并不稳健，即，输送给患者的内容可能与计算机设计的治疗计划上看到的内容显著不同，并且可能导致不可预见的临床后果。因此，调查导致适当的4D鲁棒优化方法的发展，以提高IMPT计划的最优性和鲁棒性的不确定性，包括定期和不规则的运动，是至关重要的。我们的假设是，4D鲁棒优化可以使IMPT计划对不确定性不太敏感，并实现比传统计划更好的正常组织（至少两个因素）的基础上优化的利润率。我们建议在以下特定目标中测试我们的假设：（1）量化解剖结构运动及其不确定性;（2）开发和实施4D IMPT优化;（3）增强IMPT计划稳健性;（4）验证IMPT 4D稳健优化。与IMRT中先前的4D鲁棒优化方法相比，申请人提出的研究具有几个创新方面，包括"定制的"、自发优化以处理不确定性和规则运动的小到必要的裕度，微扰理论的使用，广泛用于量子力学以求解Schr？丁格方程，以处理不规则的运动，和内存分布式并行化，以解决具有挑战性的高计算机内存需求的问题。我们希望，我们在IMPT中的开创性4D稳健优化研究将填补我们在适当方法方面的知识空白，以最大限度地减少IMPT中不确定性的影响，并为癌症患者带来显着益处，特别是那些患有胸部和腹部癌症的患者。该项目不涉及美国以外的活动或与国际合作者的伙伴关系。

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.