Abstract：
Molecular biomarkers are becoming increasingly important in the classification of intracranial gliomas. While tissue sampling remains the gold standard, there is growing interest in the use of deep learning (DL) techniques to predict these markers. This narrative review with a systematic approach identifies and synthesises the current published data on DL techniques using conventional MRI sequences for predicting isocitrate dehydrogenase (IDH) and 1p/19q-codeletion status in World Health Organisation grade 2-4 gliomas. Three databases were searched for relevant studies. In all, 13 studies met the inclusion criteria after exclusions. Key results, limitations and discrepancies between studies were synthesised. High accuracy has been reported in some studies, but the existing literature has several limitations, including generally small cohort sizes, a paucity of studies with independent testing cohorts and a lack of studies assessing IDH and 1p/19q together. While DL shows promise as a non-invasive means of predicting glioma genotype, addressing these limitations in future research will be important for facilitating clinical translation.
摘要：
分子生物标志物在颅内胶质瘤的分类中变得越来越重要。虽然组织取样仍然是黄金标准，人们对使用深度学习(DL)技术来预测这些标记越来越感兴趣。这篇系统的叙事综述使用常规MRI序列识别并合成了当前已发表的DL技术数据，以预测世界卫生组织2-4级神经胶质瘤中的异柠檬酸脱氢酶（IDH）和1p/19q-共缺失状态。搜索了三个数据库以进行相关研究。总之,13项研究在排除后符合纳入标准。关键成果,综合了研究之间的局限性和差异。在一些研究中已经报道了高精度，但是现有的文献有几个局限性，包括通常较小的队列规模，缺乏独立测试队列的研究，也缺乏一起评估IDH和1p/19q的研究。虽然DL有望作为预测神经胶质瘤基因型的非侵入性手段，在未来的研究中解决这些限制对于促进临床翻译将是重要的。
