ITR/AP: 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。放射治疗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。这也会损害健康细胞，但它们更有能力修复损伤并恢复正常功能。治疗计划的问题是指定所施加的辐射束的形状、照射时间等，以向肿瘤提供特定剂量，而不是向周围的健康组织提供过量剂量。新的医疗设备可以更好地控制辐射的特性，从而为治疗计划提供了更大的空间。然而，由于治疗设计过程的复杂性，这些设备提供最佳治疗方案的全部潜力尚未实现。通过使用先进的建模技术、最先进的优化算法和并行计算平台的实现，该项目将为放射肿瘤学家提供重要的新计算工具，用于治疗计划。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事项，并且足够强大，可以为最困难的规划问题提供良好的解决方案。该项目涉及三位研究人员的合作，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先前在治疗计划和nsf资助的解决大型优化问题的算法方面的合作基础上。参与该项目的机构包括威斯康星大学、马里兰大学医学院和芝加哥大学。

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, the University of Maryland School of Medicine, and the University of Chicago.