ArticleEuropean radiology2019
Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 2 of them syntheses that pooled it.
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Who cites it
65 citing papers in PubMed, 2 syntheses or guidelines pooled it, 96 citations in OpenAlex.
- Radiomics-based artificial intelligence models in brain tumors: A systematic review and meta-analysis of diagnostic performance.Neuroradiology · 2026Pooled it
- Pooled it
- Robust deep learning for incomplete MRI sequences in glioma grading and IDH mutation status prediction: a large-scale multicenter study.European radiology · 2026Article
- MRI-based radiomics model for predicting VEGFA expression and prognosis in lower-grade glioma.Medical physics · 2026Article
- Comparative study of radiomics features between ASL and DSC perfusion-weighted imaging in distinguishing radiation-induced brain injury from glioma recurrence.Neuroradiology · 2026Article
- Application of a multimodal MRI model integrating radiomics and habitat features for predicting glioma pathology and prognosis.BMC medical imaging · 2026Article
- The specific linear or curved boundaries between WHO grade II-III insular gliomas and the basal ganglia indicate distinct biological features, survival outcomes, and surgical strategies: evidence from 330 cases.NeuroImage. Clinical · 2026Article
- Differentiating tumor recurrence and pseudoprogression in postoperative gliomas using pseudo-continuous arterial spin labeling (pCASL) technique.BMC medical imaging · 2025Article
- A New Radiotranscriptomic Approach to Analyze Combined Sets of T3b Stage-Specific Genes and Radiomic Features in Prostate Cancer.Cancer reports (Hoboken, N.J.) · 2025Article
- Non-invasive identification of mesenchymal glioblastoma using quantitative radiomic features from advanced diffusion MRI: a preclinical-to-clinical transfer learning strategy.European radiology experimental · 2025Article
- Integrating deep learning and radiomics for preoperative glioma grading using multi-center MRI data.Scientific reports · 2025Article
- Advanced MRI, Radiomics and Radiogenomics in Unravelling Incidental Glioma Grading and Genetic Status: Where Are We?Medicina (Kaunas, Lithuania) · 2025Review
- Magnetic Resonance Imaging Radiomics-Driven Artificial Neural Network Model for Advanced Glioma Grading Assessment.Medicina (Kaunas, Lithuania) · 2025Article
- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
- Machine learning-based prediction of glioma grading.PloS one · 2025Article
- Radiomic feature reliability of amide proton transfer-weighted MR images acquired with compressed sensing at 3T.International journal of imaging systems and technology · 2024Article
- Exploring the association of glioma tumor residuals from incongruent [European journal of nuclear medicine and molecular imaging · 2024Article
- Article
- An MRI-based radiomics nomogram for preoperative prediction of Ki-67 index in nasopharyngeal carcinoma: a two-center study.Frontiers in oncology · 2024Article
- The current state of MRI-based radiomics in pituitary adenoma: promising but challenging.Frontiers in endocrinology · 2024Review
5 more citing papers are in PubMed but not listed here.
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
Abstract
purposeTo explore the feasibility and diagnostic performance of radiomics based on anatomical, diffusion and perfusion MRI in differentiating among glioma subtypes and predicting tumour proliferation.
methods220 pathology-confirmed gliomas and ten contrasts were included in the retrospective analysis. After being registered to T2FLAIR images and resampling to 1 mm
resultsIn univariate analysis, both non-wavelet and wavelet radiomics features were correlated significantly with tumour grade and the Ki-67 labelling index. The max R was 0.557 (p = 2.04E-14) in T
conclusionMulticontrast radiomics supplies complementary information on both geometric characters and molecular biological traits, which correlated significantly with tumour grade and proliferation. Combining all-contrast radiomics models might precisely predict glioma biological behaviour, which may be attributed to presurgical personal diagnosis. KEY POINTS: • Multicontrast MRI radiomics features are significantly correlated with tumour grade and Ki-67 LI. • Multimodality MRI provides independent but supplemental information in assessing glioma pathological behaviour. • Combined multicontrast MRI radiomics can precisely predict glioma subtypes and proliferation levels.
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Registered trials
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