Abstract

Info

Title:: Deep learning and radiomics in diagnosis and treatment of glioma: a review

Author(s):: YOU Huixia1; ZHANG Huailing2; 1. School of Medical Technology, Guangdong Medical University, Dongguan 523808, China 2. School of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China

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.