ArticleBioMed research international2020
The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- Diagnostic accuracy of a machine learning-based radiomics approach of MR in predicting IDH mutations in glioma patients: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Accuracy of Radiomics in PredictingRadiology. Artificial intelligence · 2024Pooled it
- Application of Enhanced T1WI of MRI Radiomics in Glioma Grading.International journal of clinical practice · 2022Trial
- Optimizing MRI sequence selection for glioma classification: a radiomics-based analysis of WHO CNS grade, final pathological diagnosis, and IDH status.La Radiologia medica · 2026Article
- Review
- Predictive power of combined inflammatory markers and magnetic resonance imaging features for glioma grading using machine learning: a retrospective study.BMC medical imaging · 2025Article
- Development of Clinical-Radiomics Nomogram for Predicting Post-Surgery Functional Improvement in High-Grade Glioma Patients.Cancers · 2025Article
- BTK Expression Level Prediction and the High-Grade Glioma Prognosis Using Radiomic Machine Learning Models.Journal of imaging informatics in medicine · 2024Article
- Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images.Clinics (Sao Paulo, Brazil) · 2023Article
- The value of multiparametric MRI radiomics in predicting IDH genotype in glioma before surgery.Frontiers in oncology · 2023Article
- A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas.Journal of clinical medicine · 2022Review
- Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics.Frontiers in neurology · 2022Review
- AI in spotting high-risk characteristics of medical imaging and molecular pathology.Precision clinical medicine · 2021Review
- Reverse Engineering Glioma Radiomics to Conventional Neuroimaging.Neurologia medico-chirurgica · 2021Review
- Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients.NPJ precision oncology · 2021Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe prognosis of IDH1-mutant glioma is significantly better than that of wild-type glioma, and the preoperative identification of IDH mutations in glioma is essential for the formulation of surgical procedures and prognostic assessment. PURPOSE: To explore the value of a radiomic model based on preoperative-enhanced MR images in the assessment of the IDH1 genotype in high-grade glioma. MATERIALS AND
methodsA retrospective analysis was performed on 182 patients with high-grade glioma confirmed by surgical pathology between December 2012 and January 2019 in our hospital with complete preoperative brain-enhanced MR images, including 79 patients with an IDH1 mutation (45 patients with WHO grade III and 34 patients with WHO grade IV) and 103 patients with wild-type IDH1 (33 patients with WHO grade III and 70 patients with WHO grade IV). Patients were divided into a primary dataset and a validation dataset at a ratio of 7 : 3 using a stratified random sampling; radiomic features were extracted using A.K. (Analysis Kit, GE Healthcare) software and were initially reduced using the Kruskal-Wallis and Spearman analyses. Lasso was finally conducted to obtain the optimized subset of the feature to build the radiomic model, and the model was then tested with cross-validation. ROC (receiver operating characteristic curve) analysis was performed to evaluate the performance of the model.
resultsThe radiomic model showed good discrimination in both the primary dataset (AUC = 0.87, 95% CI: 0.754 to 0.855, ACC = 0.798, sensitivity = 85.5%, specificity = 75.4%, positive predictive value = 0.734, and negative predictive value = 0.867) and the validation dataset (AUC = 0.86, 95% CI: 0.690 to 0.913, ACC = 0.789, sensitivity = 91.3%, specificity = 69.0%, positive predictive value = 0.700, and negative predictive value = 0.909).
conclusionThe radiomic model, based on the preoperative-enhanced MR, can effectively predict the IDH1 genotype in high-grade glioma.
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