Trial reportBMC medical imaging2023
Multiparametric MRI radiomics for the differentiation of brain glial cell hyperplasia from low-grade glioma.
Trial report in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics-based artificial intelligence models in brain tumors: A systematic review and meta-analysis of diagnostic performance.Neuroradiology · 2026Pooled it
- Current Landscape of Preclinical Models for Pediatric Gliomas: Clinical Implications and Future Directions.Cancers · 2025Review
- A Radiomic Model for Gliomas Grade and Patient Survival Prediction.Bioengineering (Basel, Switzerland) · 2025Article
- Deciphering Glioblastoma: Fundamental and Novel Insights into the Biology and Therapeutic Strategies of Gliomas.Current issues in molecular biology · 2024Review
- Multimodal Machine Learning-Based Ductal Carcinoma in situ Prediction from Breast Fibromatosis.Cancer management and research · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDifferentiating between low-grade glioma and brain glial cell hyperplasia is crucial for the customized clinical treatment of patients.
objectiveBased on multiparametric MRI imaging and clinical risk factors, a radiomics-clinical model and nomogram were constructed for the distinction of brain glial cell hyperplasia from low-grade glioma.
methodsPatients with brain glial cell hyperplasia and low-grade glioma who underwent surgery at the First Affiliated Hospital of Soochow University from March 2016 to March 2022 were retrospectively included. In this study, A total of 41 patients of brain glial cell hyperplasia and 87 patients of low-grade glioma were divided into training group and validation group randomly at a ratio of 7:3. Radiomics features were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1-enhanced). Then, LASSO, SVM, and RF models were created in order to choose a model with a greater level of efficiency for calculating each patient's Rad-score (radiomics score). The independent risk factors were identified via univariate and multivariate logistic regression analysis to filter the Rad-score and clinical risk variables in turn. A radiomics-clinical model was next built of which effectiveness was assessed.
resultsBrain glial cell hyperplasia and low-grade gliomas from the 128 cases were randomly divided into 10 groups, of which 7 served as training group and 3 as validation group. The mass effect and Rad-score were two independent risk variables used in the construction of the radiomics-clinical model, and their respective AUCs for the training group and validation group were 0.847 and 0.858. The diagnostic accuracy, sensitivity, and specificity of the validation group were 0.821, 0.750, and 0.852 respectively.
conclusionCombining with radiomics constructed by multiparametric MRI images and clinical features, the radiomics-clinical model and nomogram that were developed to distinguish between brain glial cell hyperplasia and low-grade glioma had a good performance.
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