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

提案：DMS-0427689PI：Michael C. Ferris 机构：威斯康星大学麦迪逊分校 标题：ITR-(ASE)-(dmc)：克服癌症治疗计划中的分割错误 摘要 研究人员研究了优化技术在为癌症患者设计放射治疗计划时的应用。 在外照射放射治疗中，放射束从不同角度照射到患者体内。 治疗计划过程选择光束的形状、强度和角度，使癌性肿瘤接受高剂量的辐射，同时尽可能地保护与肿瘤相邻的重要器官和正常组织。 放射治疗不是以单次剂量进行，而是在长达 9 周的时间内每日分次剂量进行。 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。 两个主要方面正在取得进展。首先，通过使用随机和非线性优化方法来构建明确考虑不确定性的治疗计划问题的公式，从而提高初始治疗计划的质量。 其次，在每次治疗期间收集的成像数据用于执行适应性放射治疗，其中治疗计划在治疗期间进行调整，以补偿器官移动、肿瘤缩小和先前治疗期间发生的其他分割错误。 实时执行所需的计算需要处理庞大的数据集和大量的计算周期——部分是在患者躺在治疗设备的沙发上时进行的。百分之四十的癌症患者接受放射治疗作为其治疗方案的关键部分，而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，以更准确地瞄准癌变区域并避免对健康组织的辐射损伤。 该项目中正在开发和实施的优化技术还可以对治疗计划进行日常调整，以考虑治疗期间内脏器官和患者运动的变化。 该项目的主要特点是使用先进的计算平台结合优化方法来提高至关重要的医疗程序的有效性。

Proposal: DMS-0427689PI: Michael C. FerrisInstitution: University of Wisconsin-MadisonTitle: ITR-(ASE)-(dmc): 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.