Collaborative Research: ITR-(ASE)-(dmc): Overcoming Fractionation Errors in Cancer Treatement Planning

合作研究：ITR-(ASE)-(dmc)：克服癌症治疗计划中的分割错误

• 批准号: 0453423
• 负责人: David Shepard
• 金额: $ 15万
• 依托单位: University of Maryland at Baltimore
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0453423&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; ASE; dmc

Proposal: DMS-0453423PI: David ShepardInstitution: University of Maryland-BaltimoreTitle: ITR-(ASE)-(dmc): Collaborative Research: Overcoming Fractionation Errors in Cancer Treatment PlanningABSTRACTThe investigators study the use of optimization techniques in designing radiation treatment plans for cancer patients. In external-beam radiotherapy, beams of radiation are aimed into the patient's body from different angles. The treatment planning process chooses the shapes, intensities, and angles of the beams such that the cancerous tumor receives a high dose of radiation, while critical organs and normal tissue adjacent to the tumor are spared, to the extent possible. Radiation is not delivered in a single dose, but rather in fractionated daily doses over a period of up to 9 weeks. The latest generation of radiation devices allows the delivery of highly optimized and accurate treatment plans. This project uses the power of distributed computing and of advances in modeling and optimization to realize the full potential of these devices. Advances are being made on two major fronts. First, the quality of the initial treatment plan is improved by using stochastic and nonlinear optimization methodology to construct formulations of the treatment planning problem that take uncertainty explicitly into account. Second, imaging data gathered at each treatment session is used to perform adaptive radiotherapy, in which the treatment plan is adjusted between sessions to compensate for organ movement, tumor shrinkage, and other fractionation errors that occurred in previous treatment sessions. Handling of huge data sets and large quantities of compute cycles is needed to perform the required calculations in real time---in part, while the patient is lying on the couch of the treatment device.Forty percent of all cancer patients receive radiation therapy as a key part of their treatment regimen, and devices that deliver radiation treatments are becoming progressively more sophisticated and powerful. This collaboration between mathematicians, computer scientists, and medical physicists is improving the effectiveness of the treatment planning process by choosing the shapes and intensities of the radiation beams used during treatments to target the cancerous regions more accurately and avoid radiation damage to healthy tissues. The optimization techniques being developed and implemented in the project also allow for day-to-day adjustment of the treatment plan, to account for changes in the internal organs and patient movement during treatment. The key feature of the project is its use of advanced computing platforms in conjunction with optimization methods to improve the effectiveness of a vitally important medical procedure.

提议：DMS-0453423PI：David Shepard研究所：马里兰大学巴尔的摩分校标题：ITR-(ASE)-(DMC)：合作研究：克服癌症治疗计划中的分割错误摘要研究人员研究优化技术在为癌症患者设计放射治疗计划中的使用。在体外放射治疗中，辐射束从不同的角度对准患者的身体。治疗计划过程选择射束的形状、强度和角度，以便癌症肿瘤接受高剂量的辐射，而关键器官和肿瘤附近的正常组织则尽可能地幸免于难。辐射不是以单次剂量提供的，而是在长达9周的时间内以分次的每日剂量提供。最新一代的放射设备可以提供高度优化和准确的治疗计划。该项目利用分布式计算的力量以及建模和优化方面的进步来实现这些设备的全部潜力。在两个主要方面正在取得进展。首先，利用随机和非线性优化方法构造显式考虑不确定性的治疗计划问题公式，以提高初始治疗计划的质量。其次，在每个治疗阶段收集的成像数据被用于执行自适应放射治疗，其中在治疗阶段之间调整治疗计划，以补偿在先前的治疗阶段中发生的器官移动、肿瘤缩小和其他分割误差。需要处理庞大的数据集和大量的计算周期来实时执行所需的计算-部分地，当患者躺在治疗设备的沙发上时。所有癌症患者中有40%接受放射治疗作为其治疗方案的关键部分，提供放射治疗的设备正变得越来越复杂和强大。数学家、计算机科学家和医学物理学家之间的这种合作正在通过选择治疗过程中使用的辐射束的形状和强度来提高治疗计划过程的有效性，以更准确地定位癌症区域，并避免辐射对健康组织的损害。该项目正在开发和实施的优化技术还允许对治疗计划进行日常调整，以考虑到治疗期间内部器官和患者活动的变化。该项目的主要特点是使用先进的计算平台和优化方法来提高至关重要的医疗程序的有效性。