Langer M, Brown R, Kijewski P, Ha C
Joint Center for Radiation Therapy, Boston, MA.
Int J Radiat Oncol Biol Phys. 1993 Jun 15;26(3):529-38. doi: 10.1016/0360-3016(93)90972-x.
An optimization algorithm improves the distribution of dose among discrete points in tissues, but tolerance depends on the distribution of dose across a continuous volume. This report asks whether an exact algorithm can be completed when enough points are taken to accurately model a dose-volume constraint.
Trials were performed using a 3-dimensional model of conformal therapy of lung cancer. Trials were repeated with different limits placed on the fraction of lung which could receive > 20 Gy. Bounds were placed on cord dose and target dose inhomogeneity. A mixed integer algorithm was used to find a feasible set of beam weights which would maximize tumor dose. Tests of feasibility and optimality are introduced to check the solution accuracy.
Solutions were optimal for points used to model tissues. An accuracy of 3-4% in a volume condition could be obtained with models of 450-600 points. The error improved to 2% with 800 points to model the lung. Solution times increased six-fold at this level of accuracy.
The mixed integer method can find optimum weights which respect dose-volume conditions in usually acceptable times. If constraints are violated by an excessive amount, the optimization model should be rerun with more points.
一种优化算法可改善组织中离散点之间的剂量分布，但耐受性取决于连续体积内的剂量分布。本报告探讨当采集足够多的点以准确模拟剂量体积约束时，是否能够完成精确算法。
使用肺癌适形治疗的三维模型进行试验。对可接受超过20 Gy照射的肺组织比例设置不同限制条件，重复进行试验。对脊髓剂量和靶区剂量不均匀性设置界限。使用混合整数算法寻找能使肿瘤剂量最大化的可行射束权重集。引入可行性和最优性测试以检查解的准确性。
对于用于模拟组织的点，解是最优的。使用450 - 600个点的模型在体积条件下可获得3 - 4%的精度。使用800个点模拟肺时，误差改善至2%。在此精度水平下，求解时间增加了六倍。
混合整数方法能够在通常可接受的时间内找到符合剂量体积条件的最优权重。如果约束被过度违反，应使用更多点重新运行优化模型。
