Ramsey Chester, Gallemore Samuel, Bowling Joseph
Thompson Cancer Survival Center, Knoxville, Tennessee, USA.
Department of Nuclear Engineering Complex, University of Tennessee, Knoxville, Tennessee, USA.
J Appl Clin Med Phys. 2025 Apr;26(4):e14608. doi: 10.1002/acm2.14608. Epub 2024 Dec 20.
Distributive stereotactic radiosurgery (dSRS) is a form of fractionation where groups of metastases are treated with a full single-fraction dose on different days. The challenge with dSRS is determining optimal target groupings to maximize the distance between targets treated in the same fraction. This study aimed to develop and validate an accessible optimization technique for distributing brain metastases into optimal treatment fractions using a genetic algorithm.
The Evolutionary Solver in Excel was used to optimize the grouping of target volumes for distributive SRS fractionation. The algorithm's performance was tested using three geometric test cases with known optimal solutions, 400 simulations with randomly distributed target volumes, and clinical data from five GammaKnife patients. The objective function was defined as the sum of average distances between target volumes within each fraction, with constraints ensuring 2-5 targets per fraction, each target being assigned to only one fraction, and a constraint on the minimum distance between any two targets in the same fraction.
The Evolutionary Solver successfully identified optimal target groupings in all geometric test cases. Compared to random groupings, the mean distance between target volumes increased by 9%, from 68.1 ± 0.8 to 74.2 ± 1.1 mm post-optimization, while the minimum distance between targets increased by 57%, from 24.9 ± 5.9 to 39.1 ± 7.5 mm. In clinical test cases, the mean distances improved from 81.6 ± 11.9 mm for manual target grouping to 85.6 ± 14.5 mm for optimized target grouping. The minimum separation improved from 35.2 ± 14.5 mm with manual grouping to 51.6 ± 14.7 mm with optimized grouping, corresponding to a mean improvement of 16.4 ± 6.1 mm.
The Evolutionary Solver in Excel provides a systematic and reproducible method for optimizing distributive target groupings in SRS and enhances spatial separation.
分布式立体定向放射外科治疗（dSRS）是一种分割放疗形式，即对转移瘤组在不同日期给予单次全剂量照射。dSRS面临的挑战是确定最佳的靶区分组，以最大化同一分割中治疗靶区之间的距离。本研究旨在开发并验证一种可利用遗传算法将脑转移瘤分配至最佳治疗分割的易获取的优化技术。
使用Excel中的进化求解器来优化分布式SRS分割的靶区体积分组。该算法的性能通过三个具有已知最佳解决方案的几何测试案例、400次随机分布靶区体积的模拟以及五例伽玛刀治疗患者的临床数据进行测试。目标函数定义为每个分割内靶区体积之间平均距离的总和，同时设置约束条件，确保每个分割有2至5个靶区，每个靶区仅分配至一个分割，并且对同一分割中任意两个靶区之间的最小距离进行约束。
进化求解器在所有几何测试案例中均成功识别出最佳靶区分组。与随机分组相比，优化后靶区体积之间的平均距离增加了9%，从68.1±0.8毫米增至74.2±1.1毫米，而靶区之间的最小距离增加了57%，从24.9±5.9毫米增至39.1±7.5毫米。在临床测试案例中，平均距离从手动靶区分组的81.6±11.9毫米改善至优化靶区分组的85.6±14.5毫米。最小间距从手动分组的35.2±14.5毫米改善至优化分组的5l.l±14.7毫米，平均改善了16.4±6.1毫米。
Excel中的进化求解器为优化SRS中的分布式靶区分组提供了一种系统且可重复的方法，并增强了空间分离效果。
