The 2021 WHO classification of CNS tumors is a challenge for neuroradiologists due to the central role of the molecular profile of tumors. The potential of novel data analysis tools in neuroimaging must be harnessed to maintain its role in predicting tumor subgroups. We performed a scoping review to determine current evidence and research gaps. A comprehensive literature search was conducted regarding glioma subgroups according to the 2021 WHO classification and the use of MRI, radiomics, machine learning, and deep learning algorithms. Sixty-two original articles were included and analyzed by extracting data on the study design and results. Only 8% of the studies included pediatric patients. Low-grade gliomas and diffuse midline gliomas were represented in one-third of the research papers. Public datasets were utilized in 22% of the studies. Conventional imaging sequences prevailed; data on functional MRI (DWI, PWI, CEST, etc.) are underrepresented. Multiparametric MRI yielded the best prediction results. IDH mutation and 1p/19q codeletion status prediction remain in focus with limited data on other molecular subgroups. Reported AUC values range from 0.6 to 0.98. Studies designed to assess generalizability are scarce. Performance is worse for smaller subgroups (e.g., 1p/19q codeleted or IDH1/2 mutated gliomas). More high-quality study designs with diversity in the analyzed population and techniques are needed.
2021年世界卫生组织中枢神经系统肿瘤分类因强调肿瘤分子谱的核心地位，对神经放射科医师提出了新挑战。为保持神经影像学在预测肿瘤亚型中的重要作用，必须充分利用新型数据分析工具的潜力。本研究通过范围综述评估当前证据并明确研究缺口。我们系统检索了关于2021年WHO分类下胶质瘤亚型，以及MRI、影像组学、机器学习和深度学习算法应用的相关文献。最终纳入62篇原创性研究，通过提取研究设计和结果数据进行系统分析。仅8%的研究包含儿童患者，低级别胶质瘤和弥漫中线胶质瘤相关研究占文献总量的三分之一。22%的研究使用了公共数据集。传统成像序列仍占主导地位，功能MRI（如DWI、PWI、CEST等）数据代表性不足。多参数MRI获得了最佳预测效果。IDH突变和1p/19q共缺失状态预测仍是研究焦点，其他分子亚型数据有限。报道的AUC值范围在0.6-0.98之间。评估模型泛化能力的研究较为缺乏，较小亚型（如1p/19q共缺失或IDH1/2突变胶质瘤）的预测性能较差。未来需要更多采用高质量研究设计、覆盖多样化人群和技术方法的研究。
肿瘤(癌症)患者之家