Xie Yuting, Zaccagna Fulvio, Rundo Leonardo, Testa Claudia, Agati Raffaele, Lodi Raffaele, Manners David Neil, Tonon Caterina
Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy.
Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bellaria Hospital, 40139 Bologna, Italy.
Diagnostics (Basel). 2022 Jul 31;12(8):1850. doi: 10.3390/diagnostics12081850.
Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63-100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0-99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.
卷积神经网络（CNN）是一种广泛应用的深度学习方法，经常被用于脑肿瘤诊断问题。此类技术在迈向临床应用的过程中仍面临一些关键挑战。这项工作的主要目标是对使用CNN架构通过磁共振图像对脑肿瘤进行分类的研究进行全面综述，目的是确定该技术发展中的有用策略和可能存在的障碍。通过预定义的系统程序识别相关文章。对于每篇文章，提取了有关训练数据、目标问题、网络架构、验证方法以及报告的定量性能标准的数据。然后，通过考虑卷积神经网络的优点以及促进CNN算法临床应用和发展需要解决的剩余挑战，评估研究的临床相关性以确定局限性。最后，为生物医学和机器学习领域的研究人员讨论了未来研究的可能方向。总共识别并综述了83项研究。它们在目标精确分类问题以及构建和训练所选CNN的策略方面存在差异。因此，报告的性能差异很大，在区分脑膜瘤、胶质瘤和垂体瘤方面（26篇文章）准确率为91.63%至100%，在区分低级别和高级别胶质瘤方面（13篇文章）准确率为60.0%至99.46%。该综述概述了基于CNN的脑肿瘤分类深度学习方法的现状。许多网络表现出良好的性能，而且没有明显证据表明任何特定的方法选择比其他方法有很大优势，特别是考虑到验证方法、性能指标和训练数据报告中存在的不一致性。很少有研究关注临床可用性。
