Neurosciences & Mental Health Research Program, The Hospital for Sick Children, Toronto, Canada.
Institute of Medical Science, University of Toronto, Toronto, Canada.
Childs Nerv Syst. 2024 Oct;40(10):3027-3035. doi: 10.1007/s00381-024-06522-5. Epub 2024 Jul 8.
Machine learning (ML) shows promise for the automation of routine tasks related to the treatment of pediatric low-grade gliomas (pLGG) such as tumor grading, typing, and segmentation. Moreover, it has been shown that ML can identify crucial information from medical images that is otherwise currently unattainable. For example, ML appears to be capable of preoperatively identifying the underlying genetic status of pLGG.
In this chapter, we reviewed, to the best of our knowledge, all published works that have used ML techniques for the imaging-based evaluation of pLGGs. Additionally, we aimed to provide some context on what it will take to go from the exploratory studies we reviewed to clinically deployed models.
Multiple studies have demonstrated that ML can accurately grade, type, and segment and detect the genetic status of pLGGs. We compared the approaches used between the different studies and observed a high degree of variability throughout the methodologies. Standardization and cooperation between the numerous groups working on these approaches will be key to accelerating the clinical deployment of these models.
The studies reviewed in this chapter detail the potential for ML techniques to transform the treatment of pLGG. However, there are still challenges that need to be overcome prior to clinical deployment.
机器学习（ML）在实现与儿科低级别胶质瘤（pLGG）治疗相关的常规任务自动化方面具有广阔前景，例如肿瘤分级、分型和分割。此外，已有研究表明，ML 可以从医学图像中识别出目前无法获取的关键信息。例如，ML 似乎能够在术前识别 pLGG 的潜在遗传状态。
在本章中，我们在力所能及的范围内，回顾了所有使用 ML 技术进行 pLGG 成像评估的已发表作品。此外，我们旨在提供一些背景信息，说明从我们回顾的探索性研究到临床部署模型需要做些什么。
多项研究表明，ML 可以准确地对 pLGG 进行分级、分型和分割，并检测其遗传状态。我们比较了不同研究中使用的方法，并观察到整个方法学中存在高度的可变性。标准化和众多研究小组之间的合作将是加速这些模型临床部署的关键。
本章回顾的研究详细说明了 ML 技术在改变 pLGG 治疗方面的潜力。然而，在临床部署之前，仍需要克服一些挑战。
