ITR/API: Collaborative Research: Cancer Treatment Plan Optimization

该项目将使用计算优化的新技术来设计癌症治疗的放射治疗计划。放射疗法将电离辐射应用于癌组织，破坏DNA并干扰癌细胞生长和分裂的能力。这也会损害健康的细胞，但它们更有能力修复损伤并恢复正常功能。治疗计划问题是指定所施加的辐射束的形状、曝光时间等，向肿瘤输送特定剂量，但不会向周围健康组织输送过量剂量。新的医疗设备允许对辐射的特性进行更大的控制，从而允许更大的治疗规划范围。然而，由于治疗设计过程的复杂性，这些设备提供最佳治疗计划的全部潜力尚未实现。通过使用先进的建模技术，国家的最先进的优化算法，并在并行计算平台上的实现，该项目将提供放射肿瘤学家与治疗计划的重要的新的计算工具。这些工具将足够灵活，以适应不同规划者和不同患者的不同优先级，并足够强大，以提供最困难的规划问题的良好解决方案。该项目涉及三名研究人员之间的合作，他们的集体专业知识包括放射肿瘤学建模，优化算法和并行实现。它建立在这些研究人员以前在治疗计划方面的合作和NSF资助的解决大型优化问题的算法方面的工作之上。参与该项目的机构是威斯康星州大学和马里兰大学医学院。

This project will use new techniques from computational optimization to design radiation therapy planning for cancer treatment. Radiation therapy applies ionizing radiation to cancerous tissue, damaging the DNA and interfering with the ability of the cancerous cells to grow and divide. This also damages healthy cells, but they are more able to repair the damage and return to normal function. The therapy planning problem is to specify the shapes of the applied radiation beams, times of exposure, etc., to deliver a specified dose to the tumor but not an excessive dose to the surrounding healthy tissue. New medical devices allow much control over the characteristics of the radiation, thus allowing much scope for the therapy planning. However, the full potential of these devices to deliver optimal treatment plans has yet to be realized due to the complexity of the treatment design process. By using advanced modeling techniques, state-of-the-art optimization algorithms, and implementations on parallel computing platforms this project will provide radiation oncologists with important new computational tools for treatment planning. These tools will be flexible enough to adapt to the varying priorities of different planners and different patients and robust enough to give good solutions to the most difficult planning problems. The project involves collaboration between three researchers whose collective expertise encompasses radiation oncology modeling, optimization algorithms, and parallel implementations. It builds on previous collaborations of these researchers on treatment planning and on NSF-funded work on algorithms for solving large optimization problems.The institutions involved in the project are the University of Wisconsin and the University ofMaryland School of Medicine.