Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Innovative Technology Of Radiotherapy Computation and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Med Phys. 2020 Jun;47(6):2329-2336. doi: 10.1002/mp.14114. Epub 2020 Mar 28.
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, for example, 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. Virtual treatment planner network 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.
Virtual treatment planner network 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.
在调强放射治疗（IMRT）的治疗计划过程中，人类规划师操作治疗计划系统（TPS）来调整治疗计划参数，例如剂量体积直方图（DVH）约束的位置和权重，以针对每个患者达成满意的计划。此过程通常耗时较长，并且计划质量取决于规划师的经验和可用的计划时间。在本研究中，我们提出通过基于深度强化学习（DRL）的虚拟治疗计划师网络（VTPN）对人类规划师的行为进行建模，以便其能够以类似于人类的方式操作 TPS 来进行治疗计划。
以前列腺癌调强放疗为例，我们使用开发的深度神经网络建立了 VTPN。我们考虑了一个带有加权二次目标函数的内部优化引擎。VTPN 设计用于观察中间计划的 DVH，并通过改变目标函数中的权重和阈值剂量来决定改善计划的操作。我们在 10 个患者病例中通过端到端 DRL 过程来训练 VTPN。计划评分用于衡量计划质量。我们在另外 64 个患者病例中展示了经过训练的 VTPN 的可行性和有效性。
VTPN 被训练为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 个测试病例中，使用初始参数时，质量评分为 4.97（±2.02），最高可能评分为 9.0。使用 VTPN 进行治疗计划可将质量评分提高到 8.44（±0.48）。
据我们所知，这是首次将外部束调强放疗中人类规划师的智能治疗计划行为自主编码到人工智能系统中。经过训练的 VTPN 能够以类似于人类的方式行事，从而生成高质量的计划。
