Personalized treatment planning with a model of radiation therapy outcomes for use in multiobjective optimization of IMRT plans for prostate cancer.
Personalized treatment planning with a model of radiation therapy outcomes for use in multiobjective optimization of IMRT plans for prostate cancer.
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DOI：
10.1186/s13014-016-0609-7
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发表时间：
2016-03-11
期刊：
影响因子：
--
通讯作者：
Phillips MH
中科院分区：
文献类型：
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作者：
Smith WP;Kim M;Holdsworth C;Liao J;Phillips MH
建立一种新的治疗计划方法，通过模拟放射治疗过程和预后指标，以实现更多以结果为中心的决策，从而超越辐射传输和IMRT优化。将内部治疗计划系统改进为包含多目标逆计划、概率结果模型和多属性决策辅助。遗传算法生成了一系列计划，体现了不同目标之间的权衡。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。马尔可夫模型计算了质量调整预期寿命（QALE），这是排名计划的终点。多目标进化算法（MOEA）被设计用来产生一个近似的帕累托前沿，代表IMRT计划的最佳权衡。来自计划剂量学的预后信息和来自患者特异性临床变量的预后信息通过影响图进行组合。计算每组患者特征的每个计划的质量评价值。进行敏感性分析以探讨患者特征和剂量学变量变化对结果的影响。该模型计算的预期寿命与一项独立的临床研究一致。提出的放射治疗模型将许多不同的物理、生物和临床模型整合为一个更全面的模型。它说明了一些可以改进的治疗计划的关键方面，并代表了对治疗过程的更详细的描述。马尔可夫模型的实施提供了剂量学变量和临床结果之间更强的联系，并可以提供一个实用的，定量的方法来做出困难的临床决策。
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.