Department of Mechanical Engineering, 10-237 Donadeo Innovation Centre for Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.
Phys Med Biol. 2019 Mar 27;64(7):075007. doi: 10.1088/1361-6560/ab075c.
Low dose rate (LDR) brachytherapy is a minimally invasive form of radiation therapy, used to treat prostate cancer, and it involves permanent implantation of radioactive sources (seeds) inside of the prostate gland. Treatment planning in brachytherapy involves a decision making process for the placement of the sources in order to deliver an effective dose of radiation to cancerous tissue in the prostate while sparing the surrounding healthy tissue. Such a decision making process can be modeled as a mixed-integer linear programming (MILP) problem. In this paper, we introduce a novel MILP optimization model framework for interstitial LDR prostate brachytherapy designed to explicitly mimic the qualities of treatment plans produced manually by expert planners. Our approach involves incorporating a unique set of clinically important constraints, called spatial constraints, into the optimization model. Computational results for an initial model reflecting clinical practice at our cancer center show that the treatment plans produced largely capture the spatial and dosimetric characteristics of manual plans created by expert planners.
低剂量率（LDR）近距离放射治疗是一种微创形式的放射治疗，用于治疗前列腺癌，它涉及放射性源（种子）在前列腺内的永久性植入。近距离放射治疗中的治疗计划涉及源的放置决策过程，以便在保护周围健康组织的同时向前列腺中的癌组织提供有效的辐射剂量。这样的决策过程可以建模为混合整数线性规划（MILP）问题。在本文中，我们引入了一种新的用于间质 LDR 前列腺近距离放射治疗的 MILP 优化模型框架，旨在明确模拟由专家规划师手动生成的治疗计划的质量。我们的方法包括将一组独特的临床重要约束（称为空间约束）纳入优化模型。反映我们癌症中心临床实践的初始模型的计算结果表明，生成的治疗计划在很大程度上捕捉了专家规划师创建的手动计划的空间和剂量学特征。
