ArticleNPJ precision oncology2021
Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients.
Article in NPJ precision oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 2 of them syntheses that pooled it.
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Who cites it
74 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predicting 1p/19q Codeletion Status in Glioma Using MRI-Derived Radiomics: A Systematic Review and Meta-Analysis of Diagnostic Accuracy.AJNR. American journal of neuroradiology · 2025Pooled it
- Machine learning radiomics for H3K27M mutation prediction in gliomas: A systematic review and meta-analysis.Neuroradiology · 2025Pooled it
- Multiparametric MRI radiomics for the differentiation of brain glial cell hyperplasia from low-grade glioma.BMC medical imaging · 2023Trial
- Robust deep learning for incomplete MRI sequences in glioma grading and IDH mutation status prediction: a large-scale multicenter study.European radiology · 2026Article
- Imaging the hallmarks of cancer.Nature reviews. Cancer · 2026Review
- Integrating Radiogenomics and CSF-Based Liquid Biopsy Sequencing for Precision Neuro-Oncology.International journal of molecular sciences · 2026Review
- Advancing precision risk stratification in adult diffuse gliomas through DSC-MRI-based habitat analysis.BMC medical imaging · 2026Article
- Article
- Spatial tumour characteristics as an indirect marker of metabolic dysregulation: evaluation for non-invasive IDH-genotyping of glioma using hybrid [18 F]FET-PET/MRI.European journal of nuclear medicine and molecular imaging · 2026Article
- Cellularity habitat-based MRI radiomics for non-invasive grading and IDH mutation prediction in adult-type diffuse glioma.BMC medical imaging · 2026Article
- Fully automated Res3DNet model to predict IDH mutation of gliomas from whole-brain MRI free of tumor segmentation.iScience · 2026Article
- Radiomics-based models to predict IDH mutation status and prognosis in gliomas using MRI: a multicenter study.Frontiers in oncology · 2026Article
- Radiomic analysis of the peritumoral zone identifies imaging signatures of glioma invasion associated with HSP70 expression.Frontiers in oncology · 2026Article
- Radiomics and clinical data predict pseudoprogression after radiotherapy in high-grade glioma.Frontiers in oncology · 2026Article
- 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
- The value of multi-parametric magnetic resonance imaging (MRI) radiomics in predicting isocitrate dehydrogenase (IDH) wildtype with telomerase reverse tranase (TERT) promoter mutation in glioma.Quantitative imaging in medicine and surgery · 2025Article
- Advanced Neuroimaging and Emerging Systemic Therapies in Glioblastoma: Current Evidence and Future Directions.Biomedicines · 2025Review
- Development and validation of radiopathomics models for predicting molecular subtypes and WHO grades in adult-type diffuse gliomas: a multicenter study.Journal of translational medicine · 2025Article
- RadGLO: an interactive platform for radiomic feature analysis and prognostic modeling in glioma.NPJ precision oncology · 2025Article
- Preoperative prediction of pituitary neuroendocrine tumor consistency based on multiparametric MRI radiomics: a multicenter study.BMC cancer · 2025Article
14 more citing papers are in PubMed but not listed here.
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15 authors.
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Abstract
Gliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images. Single-layered radiomic signatures were generated using a single MR sequence using Bayesian-regularization neural networks. Image fusion models were built by combing the significant radiomic signatures. By separately predicting the molecular markers, the predictive molecular groups were obtained. Prognostic nomograms were developed based on the predictive molecular groups and clinicopathologic data to predict progression-free survival (PFS) and overall survival (OS). The results showed that the image fusion model incorporating radiomic signatures from contrast-enhanced T1-weighted imaging (cT1WI) and apparent diffusion coefficient (ADC) achieved an AUC of 0.884 and 0.669 for predicting IDH and TERT status, respectively. cT1WI-based radiomic signature alone yielded favorable performance in predicting 1p/19q status (AUC = 0.815). The predictive molecular groups were comparable to actual ones in predicting PFS (C-index: 0.709 vs. 0.722, P = 0.241) and OS (C-index: 0.703 vs. 0.751, P = 0.359). Subgroup analyses by grades showed similar findings. The prognostic nomograms based on grades and the predictive molecular groups yielded a C-index of 0.736 and 0.735 in predicting PFS and OS, respectively. Accordingly, MRI-based radiomics may be useful for noninvasively detecting molecular groups and predicting survival in gliomas regardless of grades.
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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.