Chatigny Philippe Y, Bélanger Cédric, Poulin Éric, Beaulieu Luc
Département de physique, de génie physique et d'optique, et Centre de recherche sur le cancer, Université Laval, Québec, Quebec, Canada.
Service de physique médicale et de radioprotection, Centre intégré de cancérologie, CHU de Québec-Université Laval et Centre de recherche du CHU de Québec, Quebec, Canada.
Med Phys. 2025 Mar;52(3):1717-1727. doi: 10.1002/mp.17550. Epub 2024 Dec 10.
Recently, high-dose-rate (HDR) brachytherapy treatment plans generation was improved with the development of multicriteria optimization (MCO) algorithms that can generate thousands of pareto optimal plans within seconds. This brings a shift, from the objective of generating an acceptable plan to choosing the best plans out of thousands.
In order to choose the best plans, new criteria beyond usual dosimetrics volumes histogram (DVH) metrics are introduced and a deep learning (DL) framework is added as an automatic plan selection algorithm.
The new criteria are visual-like criteria implemented for the bladder, rectum, and urethra. One criterion also takes into account the cold spot in the prostate. Those criteria, along with commonly used DVH criteria, are used to form classes on which to train the algorithm. The algorithm is trained with an input of two 3D images, dose and mask of the anatomy, in order to rank and automatically select a plan. The confidence in the output is used for ranking and the automatic plan selection. The algorithm is trained on 835 previously treated prostate cancer patients and evaluated on a separated 20 patients cohort previously evaluated by two experts (clinical medical physicists) in an inter-observer MCO study.
The deep network takes 10 s to rank 2000 plans (vs. 5-10 min for experts to rank 4 preferred plans). A total of four different networks are trained which offer different trade-offs. The key trade-offs are the target coverage or the organs at risk (OAR) sparing. The algorithm with the best network achieves no statistical difference with the plans chosen by the two experts for 6 and 9 criteria, respectively, out of 13 criteria (paired t-test with p 0.05) while the two experts have no statistical difference between them for 7 criteria.
The developed approach is flexible since it allows the modification or addition of criteria to obtain different trade-offs in plan quality, per the institution standard. The approach is fast and robust while adding negligible time to MCO planning. These results demonstrate potential for clinical use.
最近，随着多标准优化（MCO）算法的发展，高剂量率（HDR）近距离放射治疗计划的生成得到了改进，该算法能够在数秒内生成数千个帕累托最优计划。这带来了一种转变，即从生成可接受计划的目标转变为从数千个计划中选择最佳计划。
为了选择最佳计划，引入了超出常规剂量体积直方图（DVH）指标的新标准，并添加了深度学习（DL）框架作为自动计划选择算法。
新标准是针对膀胱、直肠和尿道实施的类似视觉的标准。一个标准还考虑了前列腺中的冷区。这些标准与常用的DVH标准一起用于形成训练算法的类别。该算法通过输入两个3D图像（剂量和解剖结构的掩码）进行训练，以便对计划进行排名并自动选择计划。输出的置信度用于排名和自动计划选择。该算法在835例先前接受治疗的前列腺癌患者身上进行训练，并在一个由两名专家（临床医学物理学家）在观察者间MCO研究中先前评估过的20例患者的独立队列中进行评估。
深度网络对2000个计划进行排名需要10秒（相比之下，专家对4个优选计划进行排名需要5 - 10分钟）。总共训练了四个不同的网络，它们提供了不同的权衡。关键的权衡在于靶区覆盖或危及器官（OAR）的 sparing。具有最佳网络的算法在13个标准中的6个和9个标准上，分别与两位专家选择的计划没有统计学差异（配对t检验，p > 0.05），而两位专家在7个标准上彼此之间没有统计学差异。
所开发的方法具有灵活性，因为它允许根据机构标准修改或添加标准，以在计划质量上获得不同的权衡。该方法快速且稳健，同时在MCO规划中增加的时间可忽略不计。这些结果证明了其临床应用的潜力。
