The problem of chemotherapy treatment optimization can be defined in order to minimize the size of the tumor without endangering the patient's health; therefore, chemotherapy requires to achieve a number of objectives, simultaneously. For this reason, the optimization problem turns to a multi-objective problem. In this paper, a multi-objective meta-heuristic method is provided for cancer chemotherapy with the aim of balancing between two objectives: the amount of toxicity and the number of cancerous cells. The proposed method uses mathematical models in order to measure the drug concentration, tumor growth and the amount of toxicity. This method utilizes a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to optimize cancer chemotherapy plan using cell-cycle specific drugs. The proposed method can be a good model for personalized medicine as it returns a set of solutions as output that have balanced between different objectives and provided the possibility to choose the most appropriate therapeutic plan based on some information about the status of the patient. Experimental results confirm that the proposed method is able to explore the search space efficiently in order to find out the suitable treatment plan with minimal side effects. This main objective is provided using a desirable designing of chemotherapy drugs and controlling the injection dose. Moreover, results show that the proposed method achieve to a better therapeutic performance compared to a more recent similar method [1].
翻译:化疗优化问题可定义为在不危及患者健康的情况下最小化肿瘤体积;因此,化疗需要同时完成一些目标。由于这个原因,优化问题变成了多目标问题。本文提供了一种基于多目标元启发式算法的癌症化疗方法,旨在平衡两个目标:毒性数量和癌细胞数量。所提出的方法使用数学模型来计算药物浓度,肿瘤生长和毒性量。该方法利用多目标粒子群优化(MOPSO)算法,使用细胞周期特异性药物来优化癌症化疗计划。所提出的方法可以成为个体化医学的良好模型,因为它返回一组解作为输出,以在一定程度上基于患者状态选择最合适的治疗计划。实验结果证实,所提出的方法能够有效地探索搜索空间,以寻找合适的治疗方案并尽可能降低副作用。这个主要目的是通过良好的化疗药物设计和注射剂量控制实现的。此外,结果表明,与更近期的相似方法[1]相比,所提出的方法实现了更好的治疗性能。