OUTCOME IMPROVEMENT POTENTIAL WITH HIGHER DOSE ACCURACY

剂量精度更高，具有改善结果的潜力

• 批准号: 2895934
• 负责人: RADHE MOHAN
• 金额: $ 24.55万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-07-01 至 2001-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2895934
• 关键词: biological transport; computer simulation; head /neck neoplasm; lung neoplasms; mathematical model; model design /development; nonhuman therapy evaluation; outcomes research; patient care planning; radiation therapy dosage; radiobiology; radiopharmacology

DESCRIPTION: The main goals of this investigation are to demonstrate the following: (1) For certain treatment sites, accurate dose distributions computed with the aid of high speed Monte Carlo (MC) methods, or possibly with "non-local energy deposition" (NLED) models, will reveal over- and under-dosing and significant dose inhomogeneities relative to dose distributions computed with conventional models. The reason for the lack of accuracy of conventional models is their inability to account for the effect of the patient's internal inhomogeneities and surface irregularities on the lateral transport of radiation. (2) These deficiencies may be clinically significant and can be rectified using various techniques, especially intensity-modulated radiation therapy (IMRT), which provides greater control, facilitating the achievement of desired dose distributions. (3) Although acceptable accuracy may be achievable in many circumstances even with NLED models, ultimately a MC approach is the most appropriate one due to its universally higher accuracy, reliability and consistency of results and its potential to reduce the effort required to develop and implement dose computation systems. Achieving high accuracy of predicted dose with MC methods in acceptable times on affordable computers is now well within reach. A highly efficient MC code called PEREGRINE, developed at Lawrence Livermore National Laboratory, will be enhanced as necessary, adapted for intensity-modulated beams, tested dosimetrically, and integrated into the investigators' commercial 3D-RTP system (ADAC Pinnacle). To demonstrate the extent and magnitude of the problem, MC-based 3D conformal treatment plans will be compared with corresponding plans based on NLED and conventional dose computation methods. The plan evaluation and comparison process will utilize dose distribution displays, dose volume histograms, dose and dose-volume statistics and biophysical dose-response indices (TCP, NTCPs, equivalent uniform doses (EUDs)). Intensity distributions will be adjusted iteratively to minimize the differences between accurate (MC or NLED) dose distributions and conventional ones accepted as optimum for treatment. Special techniques will be developed for the efficient optimization of intensity distributions employing dose distributions computed with MC. The project is directed mainly at lung and head and neck sites involving cavities and complex bony struc-tures. However, sites for which surface curvature and surface dose are important may also benefit. It is hoped that in the long run the application of the proposed methods will lead to the escalation of tumor dose for the same or lower normal tissue toxicity and therefore to an overall improvement in outcome.

描述：本研究的主要目标是证明 （1）对于某些治疗部位，准确的剂量分布 借助高速蒙特卡罗（MC）方法计算，或者可能 “非局部能量沉积”（NLED）模型，将揭示过度和 剂量不足和相对于剂量的显著剂量不均匀性 用传统模型计算的分布。 不足的原因 传统模型的准确性在于它们无法解释 患者的内部不均匀性和表面不规则性 辐射的横向传输。 (2)这些缺陷可能在临床上 重要的，可以使用各种技术，特别是 调强放射治疗（IMRT），它提供了更大的 控制，便于实现所需的剂量分布。 （三） 尽管在许多情况下可以实现可接受的精度， 对于NLED模型，最终MC方法是最合适的方法，因为 其普遍较高的准确性，可靠性和一致性的结果 及其减少开发和实施 剂量计算系统。 利用MC实现预测剂量的高精度 方法在可接受的时间在负担得起的计算机上， 到达。 一个高效的MC代码，称为PEREGRINE，在劳伦斯开发， 利弗莫尔国家实验室，将加强必要的，适应 强度调制光束，剂量测试，并集成到 研究者的商用3D-RTP系统（ADAC Pinnacle）。 以证明 问题的范围和程度，基于MC的3D适形治疗计划 将与基于NLED和常规的相应计划进行比较 剂量计算方法 计划评估和比较过程将 利用剂量分布显示、剂量体积直方图、剂量和 剂量-体积统计和生物物理剂量-反应指数（TCP，NTCP， 等效均匀剂量（EUD）。 将调整强度分布 迭代以最小化准确（MC或NLED）剂量之间的差异 分布和传统的被认为是最佳的治疗。 将开发特殊技术，以有效优化 强度分布采用MC计算的剂量分布。 的 该项目主要针对肺部和头颈部， 空腔和复杂的骨结构。 然而，表面 曲率和表面剂量是重要的也可以受益。 人们希望 从长远来看，所提出的方法的应用将导致 增加肿瘤剂量以获得相同或更低的正常组织毒性， 从而导致结果的总体改善。