Affiliation:
1. Melbourne Medical School The University of Melbourne Melbourne Victoria Australia
2. Department of Cancer Imaging Peter MacCallum Cancer Centre Melbourne Victoria Australia
3. Sir Peter MacCallum Department of Oncology The University of Melbourne Melbourne Victoria Australia
4. Department of Radiology The University of Melbourne Melbourne Victoria Australia
Abstract
SummaryMolecular 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.
Subject
Radiology, Nuclear Medicine and imaging,Oncology
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献