Integrating Quantitative DCE-MRI Parameters and Radiomic Features for Improved IDH Mutation Prediction in Gliomas

Meiping Ye ¹ Zehong Cao ² Zhengyang Zhu ¹ Sixuan Chen ¹ Jianan Zhou ¹ Huiquan Yang ¹ Xin Li ¹ Qian Chen ¹ Wei Luan ¹ Ming Li ¹ Chuanshuai Tian ¹ Tianyang Sun ² Feng Shi ² Xin Zhang ^1* Bing Zhang ¹

• ¹ Department of Radiology, Nanjing Drum Tower Hospital, Nanjing, China
• ² Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China

Objectives To develop and validate a multiparametric prognostic model, incorporating Dynamic Contrast -Enhanced (DCE) quantitative parameters and multi-modality Radiomics features, for the accurate identification of isocitrate dehydrogenase 1 (IDH1) mutation status from glioma patients. Methods A 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. Radiomics 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. Five-fold cross-validation was applied for validation. Results Multivariable Combined Model showed the best prediction performance (AUC, 0.915; 95% CI: 0.87, 0.96) in the validation cohort. Multivariable Combined Model performed better than Clinical Model 1 and Radiomic Feature Model 1 (DeLong p all < 0.05), and Radiomic Feature Model 3 performed better than Radiomic Feature Model 1 (DeLong p < 0.05). Conclusions Compared 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.