Research progress of multimodal MRI radiomics and deep learning in glioma
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.
diffuse gliomamultimodalmagnetic resonance imagingradiomicsdeep learningprecise diagnosis and treatment
国家科技期刊平台
NSTL
万方数据
