ArticleScientific reports2017
Integrative Diffusion-Weighted Imaging and Radiogenomic Network Analysis of Glioblastoma multiforme.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities.EBioMedicine · 2021Trial
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- EGFR Amplification in Diffuse Glioma and Its Correlation to Language Tract Integrity.Diagnostics (Basel, Switzerland) · 2025Article
- Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.Neurosurgery · 2025Review
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Diffusion tensor imaging-based machine learning for IDH wild-type glioblastoma stratification to reveal the biological underpinning of radiomic features.CNS neuroscience & therapeutics · 2023Article
- Multi-parameter diffusion and perfusion magnetic resonance imaging and radiomics nomogram for preoperative evaluation of aquaporin-1 expression in rectal cancer.Abdominal radiology (New York) · 2022Article
- Network analysis shows decreased ipsilesional structural connectivity in glioma patients.Communications biology · 2022Article
- Radiogenomic Predictors of Recurrence in Glioblastoma-A Systematic Review.Journal of personalized medicine · 2022Review
- Identification of genetic molecular markers and immune infiltration characteristics of Alzheimer's disease through weighted gene co-expression network analysis.Frontiers in neurology · 2022Article
- Identification ofPharmacogenomics and personalized medicine · 2022Article
- Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review.Cancers · 2020Review
- Imaging signatures of glioblastoma molecular characteristics: A radiogenomics review.Journal of magnetic resonance imaging : JMRI · 2020Review
- Multiparametric and multiregional diffusion features help predict molecule information, grade and survival in lower-grade gliomas: a feasibility study.The British journal of radiology · 2019Article
- MR-spectroscopic imaging of glial tumors in the spotlight of the 2016 WHO classification.Journal of neuro-oncology · 2018Article
- Gene Expression Analysis Reveals Novel Gene Signatures Between Young and Old Adults in Human Prefrontal Cortex.Frontiers in aging neuroscience · 2018Article
- Diffusion kurtosis imaging evaluating epithelial-mesenchymal transition in colorectal carcinoma xenografts model: a preliminary study.Scientific reports · 2017Article
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11 authors.
Funding
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Abstract
In the past, changes of the Apparent Diffusion Coefficient in glioblastoma multiforme have been shown to be related to specific genes and described as being associated with survival. The purpose of this study was to investigate diffusion imaging parameters in combination with genome-wide expression data in order to obtain a comprehensive characterisation of the transcriptomic changes indicated by diffusion imaging parameters. Diffusion-weighted imaging, molecular and clinical data were collected prospectively in 21 patients. Before surgery, MRI diffusion metrics such as axial (AD), radial (RD), mean diffusivity (MD) and fractional anisotropy (FA) were assessed from the contrast enhancing tumour regions. Intraoperatively, tissue was sampled from the same areas using neuronavigation. Transcriptional data of the tissue samples was analysed by Weighted Gene Co-Expression Network Analysis (WGCNA) thus classifying genes into modules based on their network-based affiliations. Subsequent Gene Set Enrichment Analysis (GSEA) identified biological functions or pathways of the expression modules. Network analysis showed a strong association between FA and epithelial-to-mesenchymal-transition (EMT) pathway activation. Also, patients with high FA had a worse clinical outcome. MD correlated with neural function related genes and patients with high MD values had longer overall survival. In conclusion, FA and MD are associated with distinct molecular patterns and opposed clinical outcomes.
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