ArticleEuropean radiology2018
Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature.
Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 4 of them syntheses that pooled it.
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Who cites it
71 citing papers in PubMed, 4 syntheses or guidelines pooled it, 122 citations in OpenAlex.
- Radiomics and Machine Learning in Diagnostics of Glial Brain Tumors: a Systematic Review and Meta-Analysis.Sovremennye tekhnologii v meditsine · 2025Pooled it
- Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Combination of Hematoma Volume and Perihematoma Radiomics Analysis on Baseline CT Scan Predicts the Growth of Perihematomal Edema.Clinical neuroradiology · 2023Trial
- Noninvasive Prediction ofDiagnostics (Basel, Switzerland) · 2026Article
- Radiomics of T2-weighted MRI for pretreatment prediction of prognosis and temozolomide chemosensitivity in glioma.BMC cancer · 2026Article
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- Advanced MRI, Radiomics and Radiogenomics in Unravelling Incidental Glioma Grading and Genetic Status: Where Are We?Medicina (Kaunas, Lithuania) · 2025Review
- Hybrid classical and quantum computing for enhanced glioma tumor classification using TCGA data.Scientific reports · 2025Article
- Multi-dimensional interpretable deep learning-radiomics based on intra-tumoral and spatial habitat for preoperative prediction of thymic epithelial tumours risk categorisation.Acta oncologica (Stockholm, Sweden) · 2025Article
- Radiomics in glioma: emerging trends and challenges.Annals of clinical and translational neurology · 2025Review
- Radiomics-based optimization of target selection in CT-guided percutaneous lung cancer biopsy: a retrospective study.Frontiers in oncology · 2025Article
- Review
- Image Omics Nomogram Based on Incoherent Motion Diffusion-Weighted Imaging in Voxels Predicts ATRX Gene Mutation Status of Brain Glioma Patients.Journal of imaging informatics in medicine · 2024Article
- Revolutionizing Brain Tumor Care: Emerging Technologies and Strategies.Biomedicines · 2024Review
- A fusion model integrating magnetic resonance imaging radiomics and deep learning features for predicting alpha-thalassemia X-linked intellectual disability mutation status in isocitrate dehydrogenase-mutant high-grade astrocytoma: a multicenter study.Quantitative imaging in medicine and surgery · 2024Article
- Machine Learning and Radiomics in Gliomas.Advances in experimental medicine and biology · 2024Review
- Phenomic Imaging.Phenomics (Cham, Switzerland) · 2023Review
- Radiogenomics: a key component of precision cancer medicine.British journal of cancer · 2023Review
11 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo predict ATRX mutation status in patients with lower-grade gliomas using radiomic analysis.
methodsCancer Genome Atlas (TCGA) patients with lower-grade gliomas were randomly allocated into training (n = 63) and validation (n = 32) sets. An independent external-validation set (n = 91) was built based on the Chinese Genome Atlas (CGGA) database. After feature extraction, an ATRX-related signature was constructed. Subsequently, the radiomic signature was combined with a support vector machine to predict ATRX mutation status in training, validation and external-validation sets. Predictive performance was assessed by receiver operating characteristic curve analysis. Correlations between the selected features were also evaluated.
resultsNine radiomic features were screened as an ATRX-associated radiomic signature of lower-grade gliomas based on the LASSO regression model. All nine radiomic features were texture-associated (e.g. sum average and variance). The predictive efficiencies measured by the area under the curve were 94.0 %, 92.5 % and 72.5 % in the training, validation and external-validation sets, respectively. The overall correlations between the nine radiomic features were low in both TCGA and CGGA databases.
conclusionsUsing radiomic analysis, we achieved efficient prediction of ATRX genotype in lower-grade gliomas, and our model was effective in two independent databases. KEY POINTS: • ATRX in lower-grade gliomas could be predicted using radiomic analysis. • The LASSO regression algorithm and SVM performed well in radiomic analysis. • Nine radiomic features were screened as an ATRX-predictive radiomic signature. • The machine-learning model for ATRX-prediction was validated by an independent database.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.