High-Throughput Computing for a Multi-Plan Framework in Radiotherapy

放射治疗多计划框架的高吞吐量计算

• 批准号: 8271284
• 负责人: WARREN D D'SOUZA
• 金额: $ 29.73万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8271284
• 关键词: Accounting; Algorithms; Behavior; Clinical Engineering; Collection; Complex; Complication; Computational Science; Data; Decision Support Systems; Dependence; Development; Dose; Engineering; Environment; External Beam Radiation Therapy; Generations; Genetic Programming; Goals; Intensity-Modulated Radiotherapy; Knowledge; Lead; Linear Accelerator Radiotherapy Systems; Linear Programming; Machine Learning; Maps; Medical; Methods; Modeling; Monte Carlo Method; NIH Program Announcements; Organ; Patients; Physicians; Physics; Process; Property; Radiation; Radiation Oncology; Radiation therapy; Relative (related person); Research Personnel; Risk; Sampling; Shapes; Simulate; Solutions; Speed; Structure; Surface; System; Technology; Time; Toxic effect; base; cluster computing; combinatorial; computer science; computerized; computing resources; direct application; graphical user interface; heuristics; improved; innovation; insight; novel; novel strategies; predictive modeling; process optimization; programs; research clinical testing; tool; treatment planning; tumor

PROJECT SUMMARY/ABSTRACT Computerized planning for radiation delivery via either external beam radiation therapy (EBRT) or intensity- modulated radiation therapy (IMRT) from linear accelerators is a complex process involving a large amount of input data and vast numbers of decision variables. Such large-scale combinatorial optimization problems are typically intractable for conventional approaches such as the direct application of the best available commercial algorithms, and thus specialized methods that take advantage of problem structure are required. Radiation treatment planning (RTP) problems are further complicated by the fact that they are multi-objective, that is, the RTP optimization process must take into account a trade-off between the competing goals of delivering appropriate doses to the tumor and avoiding the delivery of harmful radiation to nearby healthy organs. The goal of this proposal is to harness distributive computing via the Condor system for High Throughput Computing (HTC) within an RTP environment. The specific aims for this proposal are: 1) To develop a Nested Partitions (NP) framework that guides a global search process for optimal IMRT delivery parameters using HTC. 2) To develop parallel HTC-based linear programming (LP) methods to efficiently solve the dose optimization problem in IMRT for each given set of beam angles or beam apertures. (3) To exploit a high-throughput computing (HTC) environment and the developed NP/LP/segmentation framework to efficiently generate multiple plans for each given patient case. (4) To couple this multi-plan framework with a decision support system (DSS) that includes planning surface models, a graphical-user-interface (GUI) and machine learning tools to prediction OAR complication in order to aid in the ranking and selection of the generated treatment plans. This proposal requires a multi-disciplinary approach that is best conducted within the framework of the Innovations in Biomedical Computational Science and Technology program announcement. It brings together an interdisciplinary team of investigators with expertise in medical physics, mathematical programming, industrial engineering and clinical radiation oncology that is crucial to the development of the proposed multi- plan framework using HTC in radiation therapy.

项目摘要/摘要 通过体外放射治疗(EBRT)或强度进行放射治疗的计算机计划- 直线加速器的调制放射治疗(IMRT)是一个复杂的过程，涉及大量的 输入数据和海量的决策变量。这种大规模的组合优化问题是 对于诸如直接应用最佳可用商业广告等传统方法来说，通常是难以解决的 算法，因此需要利用问题结构的专门方法。辐射 治疗计划(RTP)问题由于是多目标的事实而进一步复杂化，即， RTP优化过程必须考虑交付的相互竞争的目标之间的权衡 对肿瘤进行适当剂量的照射，避免对附近健康器官产生有害辐射。这个 该提案的目标是通过秃鹰系统利用分布式计算来实现高吞吐量 RTP环境中的计算(HTC)。 这项建议的具体目标是： 1)开发一个嵌套分区(NP)框架，指导全局搜索过程以获得最优的调强放疗 使用HTC的交付参数。 2)开发了基于HTC的并行线性规划(LP)方法，有效地解决了剂量优化问题 对于每一组给定的光束角度或光束光圈，调强放射治疗中的问题。 (3)开发高吞吐量计算(HTC)环境和开发的NP/LP/分段 框架，有效地为每个给定的患者病例生成多个计划。 (4)将该多计划框架与包括规划面的决策支持系统(DSS)相耦合 模型、图形用户界面(GUI)和机器学习工具，可按顺序预测OAR并发症 以帮助对所生成的治疗计划进行排序和选择。 这项建议需要一个多学科的方法，最好是在 生物医学计算科学与技术创新项目公告。它汇聚了 一支跨学科的研究团队，在医学物理、数学编程、 工业工程和临床放射肿瘤学，对拟议的多- HTC在放射治疗中的应用计划框架。

项目成果

A surrogate-based metaheuristic global search method for beam angle selection in radiation treatment planning.

• DOI: 10.1088/0031-9155/58/6/1933
• 发表时间: 2013-03-21
• 期刊: Physics in medicine and biology
• 影响因子: 3.5
• 作者: Zhang HH;Gao S;Chen W;Shi L;D'Souza WD;Meyer RR
• 通讯作者: Meyer RR

A two-stage sequential linear programming approach to IMRT dose optimization.

IMRT 剂量优化的两阶段顺序线性规划方法。

• DOI: 10.1088/0031-9155/55/3/022
• 发表时间: 2010
• 期刊: Physics in medicine and biology
• 影响因子: 3.5
• 作者: Zhang,HaoH;Meyer,RobertR;Wu,Jianzhou;Naqvi,ShahidA;Shi,Leyuan;D'Souza,WarrenD
• 通讯作者: D'Souza,WarrenD

Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT framework.

• DOI: 10.1016/j.ijrobp.2009.02.065
• 发表时间: 2009-08-01
• 期刊: INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
• 影响因子: 7
• 作者: Zhang, Hao H.;D'Souza, Warren D.;Si, Leyuan;Meyer, Robert R.
• 通讯作者: Meyer, Robert R.