AUTHOR=Ye Meiping , Cao Zehong , Zhu Zhengyang , Chen Sixuan , Zhou Jianan , Yang Huiquan , Li Xin , Chen Qian , Luan Wei , Li Ming , Tian Chuanshuai , Sun Tianyang , Shi Feng , Zhang Xin , Zhang Bing 

TITLE=Integrating quantitative DCE-MRI parameters and radiomic features for improved IDH mutation prediction in gliomas

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1530144

DOI=10.3389/fonc.2025.1530144

ISSN=2234-943X

ABSTRACT=ObjectivesTo develop and validate a multiparametric prognostic model, incorporating dynamic contrast-enhanced (DCE) quantitative parameters and multi-modality radiomic features, for the accurate identification of isocitrate dehydrogenase 1 (IDH1) mutation status from glioma patients.MethodsA total of 152 glioma patient data with confirmed IDH1 mutation status were retrospectively collected. A segmentation neural network was used to measure MRI quantitative parameters compared with the empirically oriented ROI selection. Radiomic features, extracted from conventional MR images (T1CE, T2W, and ADC), and DCE quantitative parameter images were combined with MRI quantitative parameters in our research to predict IDH1 mutation status. We constructed and analyzed Clinical Models 1–2 (corresponding to manual and automatic MRI quantitative parameters), Radiomic Feature Models 1–3 (corresponding to structural MRI, DCE, and multi-modality respectively), and a Multivariable Combined Model. We tried different usual classifiers and selected logistic regression according to AUC. Fivefold cross-validation was applied for validation.ResultsThe Multivariable Combined Model showed the best prediction performance (AUC, 0.915; 95% CI: 0.87, 0.96) in the validation cohort. The Multivariable Combined Model performed better than Clinical Model 1 and Radiomic Feature Model 1 (DeLong all p < 0.05), and Radiomic Feature Model 3 performed better than Radiomic Feature Model 1 (DeLong p < 0.05).ConclusionsCompared with the conventional MRI Radiomics and Clinical Models, the Multivariable Combined Model, mainly based on DCE quantitative parameters and multi-modality Radiomics features, is the most promising and deserves attention in the current study.