| Accession |
PRJCA004890 |
| Title |
Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities |
| Relevance |
Medical |
| Data types |
Whole genome sequencing
|
| Organisms |
Homo sapiens
|
| Description |
To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS.The DLS was developed based on a deep learning cohort (n = 766). The key pathways underlying the DLS were identified on a radiogenomics cohort with paired DTI and RNA-seq data (n=78), where the prognostic value of the pathway genes was validated in public databases (TCGA, n = 663; CGGA, n = 657).We found that DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes. Therapies inhibiting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS. |
| Sample scope |
Monoisolate |
| Release date |
2021-04-27 |
| Publication |
| PubMed ID |
Article title |
Journal name |
DOI |
Year |
| 34563923
|
Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities
|
EBioMedicine
|
10.1016/j.ebiom.2021.103583
|
2021
|
| 36001125
|
Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study
|
European Radiology
|
10.1007/s00330-022-09066-x
|
2022
|
|
|
| Grants |
| Agency |
program |
Grant ID |
Grant title |
| National Natural Science Foundation of China (NSFC)
|
|
U1804172
|
|
|
|
| Submitter |
Zhenyu
Zhang (fcczhangzy1@zzu.edu.cn)
|
| Organization |
The First Affiliated Hospital of Zhengzhou University |
| Submission date |
2021-04-27 |
