ArticleEuropean journal of nuclear medicine and molecular imaging2021
Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors.
Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis - a systematic review.Frontiers in neuroscience · 2024Pooled it
- A pilot study of magnetic resonance fingerprinting and radiomics analysis in autosomal dominant polycystic kidney disease.Kidney international · 2026Article
- B-mode ultrasound and contrast-enhanced ultrasound-based radiomics interpretable analysis for the prediction of macrotrabecular-massive subtype of hepatocellular carcinoma.The ultrasound journal · 2025Article
- Automated Whole-Brain Focal Cortical Dysplasia Detection Using MR Fingerprinting With Deep Learning.Neurology · 2025Article
- Time-resolved MR fingerprinting for TMagnetic resonance in medicine · 2025Article
- Radiomics in glioma: emerging trends and challenges.Annals of clinical and translational neurology · 2025Review
- Research Progress on Glioma Microenvironment and Invasiveness Utilizing Advanced Multi-Parametric Quantitative MRI.Cancers · 2024Review
- Revolutionizing Brain Tumor Care: Emerging Technologies and Strategies.Biomedicines · 2024Review
- Magnetic resonance relaxometry in quantitative imaging of brain gliomas: A literature review.The neuroradiology journal · 2024Review
- Article
- Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Multi-parametric MRI for radiotherapy simulation.Medical physics · 2023Review
- Advanced MR Techniques for Preoperative Glioma Characterization: Part 1.Journal of magnetic resonance imaging : JMRI · 2023Review
- Cardiac Magnetic Resonance Fingerprinting: Potential Clinical Applications.Current cardiology reports · 2023Review
- Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study.European radiology · 2023Article
- Contrast enhanced magnetic resonance imaging-based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study.Quantitative imaging in medicine and surgery · 2023Article
- Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives.Biomedicines · 2023Review
- Quantitative Relaxometry Metrics for Brain Metastases Compared to Normal Tissues: A Pilot MR Fingerprinting Study.Cancers · 2022Article
- The feasibility of MRI texture analysis in distinguishing glioblastoma, anaplastic astrocytoma and anaplastic oligodendroglioma.Translational cancer research · 2022Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 1 country.
Funding
Abstract
purposeThis is a radiomics study investigating the ability of texture analysis of MRF maps to improve differentiation between intra-axial adult brain tumors and to predict survival in the glioblastoma cohort.
methodsMagnetic resonance fingerprinting (MRF) acquisition was performed on 31 patients across 3 groups: 17 glioblastomas, 6 low-grade gliomas, and 8 metastases. Using regions of interest for the solid tumor and peritumoral white matter on T1 and T2 maps, second-order texture features were calculated from gray-level co-occurrence matrices and gray-level run length matrices. Selected features were compared across the three tumor groups using Wilcoxon rank-sum test. Receiver operating characteristic curve analysis was performed for each feature. Kaplan-Meier method was used for survival analysis with log rank tests.
resultsLow-grade gliomas and glioblastomas had significantly higher run percentage, run entropy, and information measure of correlation 1 on T1 than metastases (p < 0.017). The best separation of all three tumor types was seen utilizing inverse difference normalized and homogeneity values for peritumoral white matter in both T1 and T2 maps (p < 0.017). In solid tumor T2 maps, lower values in entropy and higher values of maximum probability and high-gray run emphasis were associated with longer survival in glioblastoma patients (p < 0.05). Several texture features were associated with longer survival in glioblastoma patients on peritumoral white matter T1 maps (p < 0.05).
conclusionTexture analysis of MRF-derived maps can improve our ability to differentiate common adult brain tumors by characterizing tumor heterogeneity, and may have a role in predicting outcomes in patients with glioblastoma.
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Registered trials
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