Feifei Qu
Feifei Qu has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Jiacheng Song
Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
2 shared publications - Ting Chen
Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
2 shared publications Aining Zhang
Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
1 shared publication- Aining Zhang
Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
1 shared publication Jing Deng
Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
1 shared publication- Jing Deng
Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
1 shared publication
Latest Publications
- 6 October 2025
- Published by Springer Nature in Abdominal Radiology
- Vol. 51 (5) , 2596-2605
Prediction of adverse neonatal outcomes in pregnancy-induced hypertension using IVIM-DWI-based microstructure, microcirculation, and multisequence MR texture features
Rationale and objectives Pregnancy-induced hypertension (PIH) is a common and serious pregnancy complication that threatens maternal safety and perinatal outcomes. We aimed to develop a nomogram model integrating intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) microstructure and microcirculation and multisequence MR texture features for predicting adverse outcomes in PIH. Materials and methods This retrospective study enrolled 63 PIH patients (37 with, 26 without adverse neonatal outcomes). Texture features were extracted from T1-, T2-weighted, and IVIM-DWI images. IVIM-DWI placental parameters included one DWI-based virtual magnetic resonance elastography (vMRE) parameter and six IVIM-based diffusion and perfusion parameters. Three predictive models were constructed: model 1 (texture score [T-score]) utilized multisequence texture features, model 2 incorporated IVIM-DWI parameters and model 3 (a nomogram model) combined T-score and IVIM-DWI parameters. Model performances were assessed via receiver operating characteristic (ROC) curves, area under the curve (AUC), decision curve analysis (DCA), and calibration. Results Texture analysis identified 12 features from three sequences for model 1, which demonstrated an AUC of 0.796. The placental shear modulus (μdiff) from vMRE and perfusion fraction (f) from IVIM were identified as predictors of adverse neonatal outcomes in PIH patients; model 2 using these two parameters displayed an AUC of 0.785. Model 3, the nomogram model that included T-score, μdiff, and f, exhibited enhanced predictive accuracy with an AUC of 0.844, sensitivity of 83.8%, and specificity of 73.1%. Conclusion The nomogram model constructed using placental IVIM-DWI-derived microstructural, microcirculatory, and multisequence texture features may facilitate predicting adverse neonatal outcomes in patients with PIH.
- 6 October 2025
- Published by Springer Nature in Abdominal Radiology
- Vol. 51 (5) , 2596-2605
Prediction of adverse neonatal outcomes in pregnancy-induced hypertension using IVIM-DWI-based microstructure, microcirculation, and multisequence MR texture features
Rationale and objectives Pregnancy-induced hypertension (PIH) is a common and serious pregnancy complication that threatens maternal safety and perinatal outcomes. We aimed to develop a nomogram model integrating intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) microstructure and microcirculation and multisequence MR texture features for predicting adverse outcomes in PIH. Materials and methods This retrospective study enrolled 63 PIH patients (37 with, 26 without adverse neonatal outcomes). Texture features were extracted from T1-, T2-weighted, and IVIM-DWI images. IVIM-DWI placental parameters included one DWI-based virtual magnetic resonance elastography (vMRE) parameter and six IVIM-based diffusion and perfusion parameters. Three predictive models were constructed: model 1 (texture score [T-score]) utilized multisequence texture features, model 2 incorporated IVIM-DWI parameters and model 3 (a nomogram model) combined T-score and IVIM-DWI parameters. Model performances were assessed via receiver operating characteristic (ROC) curves, area under the curve (AUC), decision curve analysis (DCA), and calibration. Results Texture analysis identified 12 features from three sequences for model 1, which demonstrated an AUC of 0.796. The placental shear modulus (μdiff) from vMRE and perfusion fraction (f) from IVIM were identified as predictors of adverse neonatal outcomes in PIH patients; model 2 using these two parameters displayed an AUC of 0.785. Model 3, the nomogram model that included T-score, μdiff, and f, exhibited enhanced predictive accuracy with an AUC of 0.844, sensitivity of 83.8%, and specificity of 73.1%. Conclusion The nomogram model constructed using placental IVIM-DWI-derived microstructural, microcirculatory, and multisequence texture features may facilitate predicting adverse neonatal outcomes in patients with PIH.