论文标题
使用混合密度网络进行自动放射治疗计划的概率剂量预测
Probabilistic dose prediction using mixture density networks for automated radiation therapy treatment planning
论文作者
论文摘要
我们证明了混合密度网络（MDN）在自动放射疗法治疗计划中的应用。结果表明，与先前在文献中先前研究的确定性方法相比，MDN可以对剂量分布进行良好的预测，并反映与固有临床折衷相关的不确定决策。针对术后前列腺术患者的一组治疗计划训练了一个两部分的高斯MDN，该计划的量度有所不同，而直肠剂量保留的优先级优先于目标覆盖率。对一组患者的检查表明，预测模式在空间和剂量体积直方图方面都遵循其各自的地面真理。基于MDN输出的一种特殊剂量模仿方法用于生成可交付的计划，从而展示了素的预测密度的可用性。因此，这种类型的MDN可能有助于支持临床医生管理临床折衷方案，并有可能提高自动化治疗计划管道生产的计划质量。
We demonstrate the application of mixture density networks (MDNs) in the context of automated radiation therapy treatment planning. It is shown that an MDN can produce good predictions of dose distributions as well as reflect uncertain decision making associated with inherently conflicting clinical tradeoffs, in contrast to deterministic methods previously investigated in literature. A two-component Gaussian MDN is trained on a set of treatment plans for postoperative prostate patients with varying extents to which rectum dose sparing was prioritized over target coverage. Examination on a test set of patients shows that the predicted modes follow their respective ground truths well both spatially and in terms of their dose-volume histograms. A special dose mimicking method based on the MDN output is used to produce deliverable plans and thereby showcase the usability of voxel-wise predictive densities. Thus, this type of MDN may serve to support clinicians in managing clinical tradeoffs and has the potential to improve quality of plans produced by an automated treatment planning pipeline.
