ArticleQuantitative imaging in medicine and surgery2024
Radiomics for predicting grades, isocitrate dehydrogenase mutation, and oxygen 6-methylguanine-DNA methyltransferase promoter methylation of adult diffuse gliomas: combination of structural MRI, apparent diffusion coefficient, and susceptibility-weighted imaging.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis.European radiology · 2026Pooled it
- Review
- Fully automated Res3DNet model to predict IDH mutation of gliomas from whole-brain MRI free of tumor segmentation.iScience · 2026Article
- Clinical and VASARI Features to PredictAJNR. American journal of neuroradiology · 2025Article
- OMT and tensor SVD-based deep learning model for segmentation and predicting genetic markers of glioma: A multicenter study.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A three-classification machine learning model for non-invasive prediction of molecular subtypes in diffuse glioma: a two-center study.Quantitative imaging in medicine and surgery · 2025Article
- Identification of intrinsic imaging subtypes using clustering analysis based on dynamic contrast-enhanced magnetic resonance imaging radiomics features for gliomas: preliminary associations with gene expression profiles.Quantitative imaging in medicine and surgery · 2025Article
- The value of morphological magnetic resonance imaging features combined with diffusion kurtosis imaging for predicting Ki-67 expression levels in high-grade gliomas.Quantitative imaging in medicine and surgery · 2025Article
- Radiomics prediction of MGMT promoter methylation in adult diffuse gliomas: a combination of structural MRI, DCE, and DTI.Frontiers in neurology · 2025Article
- Integrating quantitative DCE-MRI parameters and radiomic features for improved IDH mutation prediction in gliomas.Frontiers in oncology · 2025Article
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Abstract
Background: There has been no research investigating susceptibility-weighted imaging (SWI) radiomics features in evaluating molecular makers in gliomas. The aim of this study was to assess the predictive value of radiomics features extracted from structural magnetic resonance imaging (MRI), apparent diffusion coefficient (ADC), and SWI in determining World Health Organization (WHO) Grade, isocitrate dehydrogenase ( Methods: Retrospective MRI data of 539 patients from University of California San Francisco and Nanjing Drum Tower Hospital between January 2010 and December 2022 were analyzed in this study. The training, internal validation, and external test cohorts included 426 (median age 60 years, 168 female), 67 (median age 56 years, 31 female), and 46 (median age 55 years, 22 female) patients, respectively. A total of 7,896 radiomics features were extracted from structural MRI, ADC, and SWI within two regions of interest (ROIs). Feature selection was conducted using analysis of variance (ANOVA) F-test, and random forest was employed to establish predictive models. Chi-square test and Mann-Whitney Results: For WHO Grade task, the combined model of structural MRI, ADC, and SWI achieved the highest AUC of 0.951 [95% confidence interval (CI): 0.886-1.000] on the external test cohort. For IDH mutation task, the structural MRI model achieved the highest AUC of 0.917 (95% CI: 0.801-1.000) on the external test cohort. For MGMT task, the combined model of structural MRI and ADC achieved the highest AUC of 0.650 (95% CI: 0.485-0.814) on the internal validation cohort. Conclusions: The combined structural MRI, ADC, and SWI models achieved promising performance in assessing WHO Grade and
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