OPTIMIZATION OF 3D CONFORMAL TREATMENTS

很明显，三维放射治疗计划 （3DRTP）对于实现更高的治愈率至关重要。 早期经验 3DRTP已经证明了它的潜力和局限性。 一个主要 3DRTP的困难在于规划数据量大， 很难理解，要探索的选择数量是 大大增加。 因此，计划的设计和评估是 很难，几乎不可能探索足够大的 通过试验和错误来解决问题。 因此， 三维治疗计划不能实现， 计算机辅助优化的应用。 为了优化 过程是有意义的，它必须包括临床结果， 射束变化引起的剂量分布变化 特色 我们假设，即使模型 预测临床结果过于简单， 已知用于做出这种预测的生物数据是稀疏的， 完全可靠，将临床和生物学数据纳入 优化过程将引导治疗计划的解决方案 问题在于降低正常组织并发症， 肿瘤控制 我们建议设计和开发形式主义，算法和 三维适形治疗临床相关优化软件 布局 我们还建议开发新的生物模型。 我们将使用剂量 分布数据、生物模型和观察到的正常组织和肿瘤 计算肿瘤控制和正常组织并发症的响应数据 概率为每个关键正常结构中遇到的， 治疗方案 这些数量将被合并为一个单一的分数 使用一个目标函数，该函数将包含每个 医生评估的终点。 使用“模拟退火” 优化的方法，波束（或射线）权重将被调整为 最大化得分。 一般来说，需要额外的限制。 应用以确保优化结果与 医生的判断。 我们建议应用优化过程 两类适形治疗计划问题：（1）治疗 由一个或多个自动传送的字段组成，每个字段由 多个多叶准直器形状的共形段， 确定字段和段的权重的最佳集合，以及（2） 治疗由少量强度调制场组成， 通过调整“射线权重”来设计最佳注量模式。 这些方法在我们的初步研究中显示出相当大的前景。 调查事务所

It is becoming evident that three-dimensional radiation treatment planning (3DRTP) is essential for achieving higher cure rates. Early experience with 3DRTP has demonstrated its potential as well as limitations. A major difficulty in 3DRTP is that the planning data are voluminous, displays are difficult to comprehend and the number of options to be explored are greatly increased. Consequently, design and evaluation of plans is difficult and it is virtually impossible to explore a sufficiently large domain of solutions by trial and error. Thus, full clinical potential of three-dimensional treatment planning cannot be realized without the application of computer-aided optimization. In order for the optimization process to be meaningful, it must incorporate clinical consequences of changes in dose distributions resulting from changes in beam characteristics. We hypothesize that, even though the models for predicting clinical consequences are simplistic and the clinical and biological data for making such predications are known to be sparse and not entirely reliable, incorporation of clinical and biological data into the optimization process will steer the solution of the treatment planning problem in the direction of lower normal tissue complications and higher tumor control. We propose to design and develop formalisms, algorithms and software for clinically relevant optimization of 3D conformal treatment plans. We also propose to develop new biological models. We will use dose distribution data, biological models and observed normal tissue and tumor response data to compute tumor control and normal tissue complication probabilities for each of the critical normal structures encountered in a treatment plan. These quantities will be combined into a single score using an objective function which will incorporate the importance of each end point as assessed by the physician. Using the "simulated annealing" method of optimization, the beam (or ray) weights will be adjusted to maximize the score. In general, additional constraints will need to be applied to ensure consistency of the results of optimization with the judgement of the physician. We propose to apply the optimization process to two classes of conformal treatment planning problems: (1) treatments consisting of one or more automatically delivered fields, each consisting of a number of multi-leaf collimator shaped conformal segments in which the optimum set of weights of fields and segments are determined, and (2) treatments consisting of a small number of intensity-modulated fields in which optimum fluence patterns are designed by adjusting "ray weights". These approaches have shown considerable promise in our preliminary investigations.