Deep learning and radiomics in diagnosis and treatment of glioma:a review
10.3969/j.issn.1005-202X.2023.12.008
- VernacularTitle:深度学习和影像组学在脑胶质瘤诊疗中的研究进展
- Author:
Huixia YOU
1
;
Huailing ZHANG
Author Information
1. 广东医科大学医学技术学院,广东东莞 523808
- Keywords:
glioma;
deep learning;
radiomics;
review
- From:
Chinese Journal of Medical Physics
2023;40(12):1502-1508
- CountryChina
- Language:Chinese
-
Abstract:
Deep learning can automatically learn representative features from image data for clinical analysis,such as glioma staging/grading,prediction of molecular marker status,differentiation of tumor pseudoprogression from true progression,and survival prediction.Radiomics aims to quantitatively describe tumors based on imaging features extracted from routine medical images,and it can capture small changes in tissues and lesions,such as heterogeneity within tumor volume,tumor shape,and their changes over time during serial imaging.Image analysis technology based on radiomics and deep learning can simplify and automate the diagnosis and treatment of glioma,with high accuracy.The review gives a brief introduction of radiomics methods and deep learning technologies,and then summarizes the application of radiomics methods and deep learning technologies in the diagnosis and treatment of glioma in recent years,expecting to provide a preoperative basis for the treatment scheme selection for glioma patients.
