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.

Public Health Relevance

The goal of this proposal is to develop a multi-dimensional platform for sophisticated treatment planning of radiation delivery. It will develop novel algorithms that will enable generation of superior treatment plans with the added advantage of increasing the speed of treatment planning. Further, it will allow physicians to know beforehand the quality of the treatment plan relative to the multiple treatment objectives and be able to determine the treatment complication scenario beforehand.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
Project #
Application #
Study Section
Radiation Therapeutics and Biology Study Section (RTB)
Program Officer
Deye, James
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Maryland Baltimore
Schools of Medicine
United States
Zip Code
Zhang, H H; Gao, S; Chen, W et al. (2013) A surrogate-based metaheuristic global search method for beam angle selection in radiation treatment planning. Phys Med Biol 58:1933-46
Zhang, Hao H; Meyer, Robert R; Shi, Leyuan et al. (2010) The minimum knowledge base for predicting organ-at-risk dose-volume levels and plan-related complications in IMRT planning. Phys Med Biol 55:1935-47
Zhang, Hao H; Meyer, Robert R; Wu, Jianzhou et al. (2010) A two-stage sequential linear programming approach to IMRT dose optimization. Phys Med Biol 55:883-902
Zhang, Hao H; D'Souza, Warren D; Shi, Leyuan et al. (2009) Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT framework. Int J Radiat Oncol Biol Phys 74:1617-26