Trial reportInternational journal of clinical practice2022
Application of Enhanced T1WI of MRI Radiomics in Glioma Grading.
Trial report in International journal of clinical practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis - a systematic review.Frontiers in neuroscience · 2024Pooled it
- Glioma Grade Classification Using Machine Learning and MRI Radiomics: A Single-Center Prospective Study Comparing Original and Wavelet-Transformed Features From Anatomical, Diffusion-Weighted, and Post-Contrast Imaging.Health science reports · 2026Article
- Image Feature Fusion of Hyperspectral Imaging and MRI for Automated Subtype Classification and Grading of Adult Diffuse Gliomas According to the 2021 WHO Criteria.Diagnostics (Basel, Switzerland) · 2026Article
- 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
- Prevalence and Determinants of the Pool Sign in Lung Cancer Patients with Brain Metastasis.Current medical imaging · 2025Article
- Glioma Grading by Integrating Radiomic Features from Peritumoral Edema in Fused MRI Images and Automated Machine Learning.Journal of imaging · 2025Article
- Exploring the Predictive Value of Grading in Regions Beyond Peritumoral Edema in Gliomas Based on Radiomics.Current medical imaging · 2025Article
- Magnetic Resonance Imaging Radiomics-Driven Artificial Neural Network Model for Advanced Glioma Grading Assessment.Medicina (Kaunas, Lithuania) · 2025Article
- Study on an Automatic Classification Method for Determining the Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-Scale Spatial-Spectral Fusion Features.Sensors (Basel, Switzerland) · 2024Article
- MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study.Frontiers in oncology · 2024Article
- Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation.Diagnostics (Basel, Switzerland) · 2023Article
- Development and External Validation of a Radiomics Model Derived from Preoperative Gadoxetic Acid-Enhanced MRI for Predicting Histopathologic Grade of Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2023Article
- Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping.Bioengineering (Basel, Switzerland) · 2023Article
- Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images.Clinics (Sao Paulo, Brazil) · 2023Article
- Assessment of the emerging role of AI in diagnosing gliomas using MRI: Systematic review and meta-analysis.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To explore the application value of the radiomics method based on enhanced T1WI in glioma grading. Materials and Methods: A retrospective analysis was performed using data of 114 patients with glioma, which was confirmed using surgery and pathological tests, at our hospital between January 2017 and November 2020. The patients were randomly divided into the training and test groups in a ratio of 7 : 3. The Analysis Kit (AK) software was used for radiomic analysis, and a total of 461 tumor texture features were extracted. Spearman correlation analysis and the least absolute shrinkage and selection (LASSO) algorithm were employed to perform feature dimensionality reduction on the training group. A radiomics model was then constructed for glioma grading, and the validation group was used for verification. Results: The area under the ROC curve (AUC) of the proposed model was calculated to identify its performance in the training group, which was 0.95 (95% CI = 0.905-0.994), accuracy was 84.8%, sensitivity was 100%, and specificity was 77.8%. The AUC of the validation group was 0.952 (95% CI = 0.871-1.000), accuracy was 93.9%, sensitivity was 90.0%, and specificity was 95.6%. Conclusions: The radiomics model based on enhanced T1WI improved the accuracy of glioma grading and better assisted clinical decision-making.
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