Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures

Frontiers in Oncology

Xinhua Hu¹  Wenbin Zhang¹  Jiu Chen¹  Hongyi Liu¹  Honglin Ge²  Yong Liu²  Guanjie Hu²  Dongming Liu²  Kun Yang² 

[1] Department of Neurosurgery, Institute of Brain Sciences, The Affilated Nanjing Brain Hosptial of Nanjing Medical University, Nanjing, China;Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China;Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China;

关键词: glioblastoma;    radiomics;    imaging genomics;    artificial intelligence;    machine learning;    deep learning;   

DOI  :  10.3389/fonc.2021.699265

来源: DOAJ

【 摘 要 】

Based on artificial intelligence (AI), computer-assisted medical diagnosis can scientifically and efficiently deal with a large quantity of medical imaging data. AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis. Imaging-genomics attempts to explore the associations between potential gene expression patterns and specific imaging phenotypes. These associations provide potential cellular pathophysiology information, allowing sampling of the lesion habitat with high spatial resolution. Glioblastoma (GB) poses spatial and temporal heterogeneous characteristics, challenging to current precise diagnosis and treatments for the disease. Imaging-genomics provides a powerful tool for non-invasive global assessment of GB and its response to treatment. Imaging-genomics also has the potential to advance our understanding of underlying cancer biology, gene alterations, and corresponding biological processes. This article reviews the recent progress in the utilization of the imaging-genomics analysis in GB patients, focusing on its implications and prospects in individualized diagnosis and management.

【 授权许可】

Unknown