Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.
Royal Melbourne Hospital, Melbourne, Australia.
Neurosurgery. 2022 Jul 1;91(1):8-26. doi: 10.1227/neu.0000000000001938. Epub 2022 Mar 31.
Survival prediction of patients affected by brain tumors provides essential information to guide surgical planning, adjuvant treatment selection, and patient counseling. Current reliance on clinical factors, such as Karnofsky Performance Status Scale, and simplistic radiological characteristics are, however, inadequate for survival prediction in tumors such as glioma that demonstrate molecular and clinical heterogeneity with variable survival outcomes. Advances in the domain of artificial intelligence have afforded powerful tools to capture a large number of hidden high-dimensional imaging features that reflect abundant information about tumor structure and physiology. Here, we provide an overview of current literature that apply computational analysis tools such as radiomics and machine learning methods to the pipeline of image preprocessing, tumor segmentation, feature extraction, and construction of classifiers to establish survival prediction models based on neuroimaging. We also discuss challenges relating to the development and evaluation of such models and explore ethical issues surrounding the future use of machine learning predictions.
脑肿瘤患者的生存预测为指导手术规划、辅助治疗选择和患者咨询提供了重要信息。然而，目前仅依赖于临床因素（如 Karnofsky 表现状态量表）和简单的影像学特征，对于表现出分子和临床异质性、生存结果不同的肿瘤（如胶质瘤）的生存预测是不够的。人工智能领域的进步为捕捉大量隐藏的高维成像特征提供了强大的工具，这些特征反映了肿瘤结构和生理学的丰富信息。在这里，我们提供了当前文献的概述，这些文献应用了计算分析工具，如放射组学和机器学习方法，来构建图像预处理、肿瘤分割、特征提取和分类器构建的流水线，以基于神经影像学建立生存预测模型。我们还讨论了与这些模型的开发和评估相关的挑战，并探讨了机器学习预测未来使用所涉及的伦理问题。
