Smith Wade P, Kim Minsun, Holdsworth Clay, Liao Jay, Phillips Mark H
Department of Radiation Oncology, University of Washington Medical Center, 1959 NE Pacific St, Box 356043, Seattle, 98115, WA, USA.
Brigham and Women's Hospital, 75 Francis St., Boston, 02115, MA, USA.
Radiat Oncol. 2016 Mar 11;11:38. doi: 10.1186/s13014-016-0609-7.
To build a new treatment planning approach that extends beyond radiation transport and IMRT optimization by modeling the radiation therapy process and prognostic indicators for more outcome-focused decision making.
An in-house treatment planning system was modified to include multiobjective inverse planning, a probabilistic outcome model, and a multi-attribute decision aid. A genetic algorithm generated a set of plans embodying trade-offs between the separate objectives. An influence diagram network modeled the radiation therapy process of prostate cancer using expert opinion, results of clinical trials, and published research. A Markov model calculated a quality adjusted life expectancy (QALE), which was the endpoint for ranking plans.
The Multiobjective Evolutionary Algorithm (MOEA) was designed to produce an approximation of the Pareto Front representing optimal tradeoffs for IMRT plans. Prognostic information from the dosimetrics of the plans, and from patient-specific clinical variables were combined by the influence diagram. QALEs were calculated for each plan for each set of patient characteristics. Sensitivity analyses were conducted to explore changes in outcomes for variations in patient characteristics and dosimetric variables. The model calculated life expectancies that were in agreement with an independent clinical study.
The radiation therapy model proposed has integrated a number of different physical, biological and clinical models into a more comprehensive model. It illustrates a number of the critical aspects of treatment planning that can be improved and represents a more detailed description of the therapy process. A Markov model was implemented to provide a stronger connection between dosimetric variables and clinical outcomes and could provide a practical, quantitative method for making difficult clinical decisions.
通过对放射治疗过程和预后指标进行建模，构建一种超越辐射传输和调强放疗优化的新治疗计划方法，以实现更聚焦于治疗结果的决策。
对内部治疗计划系统进行修改，使其包括多目标逆向计划、概率性结果模型和多属性决策辅助工具。遗传算法生成一组体现不同目标之间权衡的计划。影响图网络利用专家意见、临床试验结果和已发表的研究对前列腺癌的放射治疗过程进行建模。马尔可夫模型计算质量调整生命预期（QALE），将其作为计划排序的终点。
多目标进化算法（MOEA）旨在生成帕累托前沿的近似值，以代表调强放疗计划的最优权衡。通过影响图将计划剂量学和患者特定临床变量的预后信息结合起来。针对每组患者特征，为每个计划计算QALE。进行敏感性分析以探索患者特征和剂量学变量变化时结果的变化。该模型计算出的生命预期与一项独立临床研究结果一致。
所提出的放射治疗模型已将许多不同的物理、生物和临床模型整合为一个更全面的模型。它阐明了治疗计划中一些可改进的关键方面，并对治疗过程进行了更详细的描述。实施马尔可夫模型以加强剂量学变量与临床结果之间的联系，并可为做出困难的临床决策提供一种实用的定量方法。
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