Artificial Intelligence Applications in Glioma With 1p/19q Co-Deletion: A Systematic Review.
Simin ZhangLijuan YinLu MaHuaiqiang SunPublished in: Journal of magnetic resonance imaging : JMRI (2023)
As an important genomic marker for oligodendrogliomas, early determination of 1p/19q co-deletion status is critical for guiding therapy and predicting prognosis in patients with glioma. The purpose of this study is to systematically review the literature concerning the magnetic resonance imaging (MRI) with artificial intelligence (AI) methods for predicting 1p/19q co-deletion status in glioma. PubMed, Scopus, Embase, and IEEE Xplore were searched in accordance with the Preferred Reporting Items for systematic reviews and meta-analyses guidelines. Methodological quality of studies was assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2. Finally, 28 studies were included in the quantitative analysis. Diagnostic test accuracy reached an area under the ROC curve of 0.71-0.98 were reported in 24 studies. The remaining four studies with no available AUC provided an accuracy of 0.75-0. 89. The included studies varied widely in terms of imaging sequences, input features, and modeling methods. The current review highlighted that integrating MRI with AI technology is a potential tool for determination 1p/19q status pre-operatively and noninvasively, which can possibly help clinical decision-making. However, the reliability and feasibility of this approach still need to be further validated and improved in a real clinical setting. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: 2.
Keyphrases
- artificial intelligence
- magnetic resonance imaging
- machine learning
- big data
- case control
- systematic review
- deep learning
- meta analyses
- contrast enhanced
- emergency department
- stem cells
- risk assessment
- randomized controlled trial
- photodynamic therapy
- mesenchymal stem cells
- genome wide
- copy number
- magnetic resonance
- quality improvement
- dna methylation
- mass spectrometry
- diffusion weighted imaging
- tandem mass spectrometry