Advances in deep learning and MRI co-assisted genotyping of brain gliomas
10.16016/j.2097-0927.202409134
- VernacularTitle:深度学习和磁共振图像辅助脑胶质瘤基因分型的研究进展
- Author:
Shaoguo CUI
1
;
Junshan CHEN
Author Information
1. 重庆师范大学计算机与信息科学学院
- Keywords:
deep learning;
brain glioma;
genotyping;
magnetic resonance imaging
- From:
Journal of Army Medical University
2025;47(14):1557-1567
- CountryChina
- Language:Chinese
-
Abstract:
The genotypic status of gliomas is a critical indicator for assessing patient prognosis and survival duration,with its genetic classification holding significant clinical value.Although biomolecular markers serve as essential criteria for glioma classification,challenges such as high detection costs and low efficiency persist.In recent years,the rapid advancement of artificial intelligence(AI)has provided novel solutions for the automated classification of gliomas,with MRI-based deep learning techniques for genetic status prediction emerging as a research hotspot.Current studies have established a technical framework ranging from supervised to unsupervised learning by integrating multimodal MRI data,achieving high predictive accuracy in single-gene classification through multi-task joint optimization and transfer learning.Meanwhile,multi-gene joint analysis,as an emerging direction,has yielded preliminary exploratory results.In the future,key breakthroughs in glioma genetic classification research will include the development of multi-gene synchronous prediction models,enhancing generalization capabilities for small samples through generative data augmentation,improving prediction accuracy using multi-source heterogeneous data and large models,and introducing visualization techniques to enhance interpretability.
