磁共振成像2024，Vol.15Issue(7)：165-172,8.DOI:10.12015/issn.1674-8034.2024.07.028
多模态MRI影像组学及深度学习在胶质瘤诊疗中的研究进展
Research progress of multimodal MRI radiomics and deep learning in glioma
摘要
Abstract
Diffuse gliomas are the most common primary malignant tumors of the brain,and preoperative precise grading and molecular typing prediction are crucial for developing appropriate treatment strategies and predicting survival rates. Imaging omics uses advanced feature analysis to extract data from medical images and construct predictive models to capture small changes in lesions,thereby improving the accuracy of clinical diagnosis,prognosis assessment,and treatment response prediction. Deep learning can automatically learn meaningful features for research,and can automatically learn and extract multi-layer features from a large amount of raw data,rather than manually made shallow features. As deep learning has been fully proven to accurately find very deep and abstract features,it has become a widely studied topic in the field of medical image analysis. With the advancement of computing power,deep learning based artificial intelligence has completely changed various fields. Promote the biological validation of radiomic features in gliomas. This study provides a review of the latest research on multimodal MRI radiomics and deep learning in preoperative grading,molecular typing,survival prediction,and treatment evaluation of glioma,with the aim of providing accurate diagnosis and treatment for glioma patients.关键词
弥漫性胶质瘤/多模态/磁共振成像/影像组学/深度学习/精准治疗Key words
diffuse glioma/multimodal/magnetic resonance imaging/radiomics/deep learning/precise diagnosis and treatment分类
医药卫生引用本文复制引用
王茹,高阳..多模态MRI影像组学及深度学习在胶质瘤诊疗中的研究进展[J].磁共振成像,2024,15(7):165-172,8.基金项目
内蒙古自治区科技计划项目(编号:2019GG047) Science and Technology Plan Project of Inner Mongolia Autonomous Region(No.2019GG047). （编号:2019GG047）
