School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
Department of Medical Physics, BC Cancer - Vancouver Centre, Vancouver, BC, Canada.
Int J Comput Assist Radiol Surg. 2021 Jul;16(7):1161-1170. doi: 10.1007/s11548-021-02405-1. Epub 2021 May 29.
In low-dose-rate prostate brachytherapy (LDR-PB), treatment planning is the process of determining the arrangement of implantable radioactive sources that radiates the prostate while sparing healthy surrounding tissues. Currently, these plans are prepared manually by experts incorporating the centre's planning style and guidelines. In this article, we develop a novel framework that can learn a centre's planning strategy and automatically reproduce rapid clinically acceptable plans.
The proposed framework is based on conditional generative adversarial networks that learn our centre's planning style using a pool of 931 historical LDR-PB planning data. Two additional losses that help constrain prohibited needle patterns and produce similar-looking plans are also proposed. Once trained, this model generates an initial distribution of needles which is passed to a planner. The planner then initializes the sources based on the predicted needles and uses a simulated annealing algorithm to optimize their locations further.
Quantitative analysis was carried out on 170 cases which showed the generated plans having similar dosimetry to that of the manual plans but with significantly lower planning durations. Indeed, on the test cases, the clinical target volumes achieving [Formula: see text] of the prescribed dose for the generated plans was on average [Formula: see text] ([Formula: see text] for manual plans) with an average planning time of [Formula: see text] min ([Formula: see text] min for manual plans). Further qualitative analysis was conducted by an expert planner who accepted [Formula: see text] of the plans with some changes ([Formula: see text] requiring minor changes & [Formula: see text] requiring major changes).
The proposed framework demonstrated the ability to rapidly generate quality treatment plans that not only fulfil the dosimetric requirements but also takes into account the centre's planning style. Adoption of such a framework would save significant amount of time and resources spent on every patient; boosting the overall operational efficiency of this treatment.
在低剂量率前列腺近距离放射治疗（LDR-PB）中，治疗计划是确定可放射性治疗前列腺同时保护周围健康组织的植入放射性源排列的过程。目前，这些计划是由专家根据中心的规划风格和指南手动制定的。在本文中，我们开发了一种新的框架，可以学习中心的规划策略并自动生成快速临床可接受的计划。
所提出的框架基于条件生成对抗网络，该网络使用 931 个历史 LDR-PB 规划数据的池来学习我们中心的规划风格。还提出了两个额外的损失，有助于限制禁止的针模式并生成相似的计划。一旦训练完成，该模型会生成初始针分布，然后将其传递给规划器。规划器根据预测的针初始设置源，并使用模拟退火算法进一步优化其位置。
对 170 例病例进行了定量分析，结果表明生成的计划具有与手动计划相似的剂量分布，但规划时间明显更短。实际上，在测试病例中，生成的计划中临床靶区达到规定剂量的[Formula: see text]的比例平均为[Formula: see text]（手动计划为[Formula: see text]），平均规划时间为[Formula: see text]分钟（手动计划为[Formula: see text]分钟）。一位专家规划师进行了进一步的定性分析，他接受了[Formula: see text]的计划并进行了一些更改（[Formula: see text]需要较小的更改，[Formula: see text]需要较大的更改）。
所提出的框架展示了快速生成不仅满足剂量要求，还考虑到中心规划风格的高质量治疗计划的能力。采用这样的框架将节省为每位患者花费的大量时间和资源；提高这种治疗的整体运营效率。
