Operating a treatment planning system using a deep-reinforcement learning-based virtual treatment planner for prostate cancer intensity-modulated radiation therapy treatment planning.
Operating a treatment planning system using a deep-reinforcement learning-based virtual treatment planner for prostate cancer intensity-modulated radiation therapy treatment planning.
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DOI：
10.1002/mp.14114
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发表时间：
2020-06
期刊：
影响因子：
3.8
通讯作者：
Jia X
中科院分区：
文献类型：
--
作者：
Shen C;Nguyen D;Chen L;Gonzalez Y;McBeth R;Qin N;Jiang SB;Jia X
在调强放射治疗（IMRT）的治疗计划过程中，计划人员通过调整治疗计划系统（TPS）中的剂量体积直方图（DVH）约束的位置和权重等治疗计划参数，为每个患者制定一个满意的计划。这个过程通常是耗时的，计划的质量取决于计划者的经验和可用的计划时间。在这项研究中，我们提出通过基于深度强化学习（DRL）的虚拟治疗计划者网络（VTPN）对人类计划者在治疗计划中的行为进行建模，以便能够以类似人类的方式操作TPS进行治疗计划。以前列腺癌IMRT为例，我们使用开发的深度神经网络建立了VTPN。我们考虑了一个内部的优化引擎与加权二次目标函数。VTPN的目的是观察一个中间计划DVH，并决定采取行动，以改善计划，通过改变目标函数中的权重和阈值剂量。我们在10例患者病例中对VTPN进行了端到端DRL过程培训。计划评分用于衡量计划质量。我们在另外64例患者中证明了训练的VTPN的可行性和有效性。VTPN接受培训，自发学习如何调整治疗计划参数，以生成高质量的治疗计划。在64个测试用例中，初始化参数，质量评分为4.97（± 2.02），其中9.0为最高可能评分。使用VTPN进行治疗计划将质量评分提高到8.44（± 0.48）。据我们所知，这是第一次在人工智能系统中自主编码外部射束IMRT中人类计划者的智能治疗计划行为。经过训练的VTPN能够以类似人类的方式进行操作，以生成高质量的计划。
In the treatment planning process of intensity modulated radiation therapy (IMRT), a human planner operates the treatment planning system (TPS) to adjust treatment planning parameters, e.g. dose volume histogram (DVH) constraints’ locations and weights, to achieve a satisfactory plan for each patient. This process is usually time-consuming, and the plan quality depends on planer’s experience and available planning time. In this study, we proposed to model the behaviors of human planners in treatment planning by a deep reinforcement learning (DRL)-based virtual treatment planner network (VTPN), such that it can operate the TPS in a human-like manner for treatment planning. Using prostate cancer IMRT as an example, we established the VTPN using a deep neural network developed. We considered an in-house optimization engine with a weighted quadratic objective function. VTPN was designed to observe an intermediate plan DVHs and decide the action to improve the plan by changing weights and threshold dose in the objective function. We trained the VTPN in an end-to-end DRL process in 10 patient cases. A plan score was used to measure plan quality. We demonstrated the feasibility and effectiveness of the trained VTPN in another 64 patient cases. VTPN was trained to spontaneously learn how to adjust treatment planning parameters to generate high-quality treatment plans. In the 64 testing cases, with initialized parameters, quality score was 4.97 (±2.02), with 9.0 being the highest possible score. Using VTPN to perform treatment planning improved quality score to 8.44 (±0.48). To our knowledge, this was the first time that intelligent treatment planning behaviors of human planner in external beam IMRT are autonomously encoded in an artificial intelligence system. The trained VTPN is capable of behaving in a human-like way to produce high-quality plans.
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