MRI-based deep learning-radiomics ensemble model for predicting postpartum hemorrhage in high-risk pregnancies
10.3969/j.issn.1005-202X.2025.11.018
- VernacularTitle:基于MRI的深度影像组学集成学习预测高风险产妇产后出血
- Author:
Qi ZHANG
1
;
Haijie WANG
;
Xiaoyun LIANG
;
Hao ZHU
;
Guang YANG
Author Information
1. 华东师范大学物理与电子科学学院上海市磁共振重点实验室,上海 200062;华东师范大学医学磁共振与分子影像技术研究院,上海 200062
- Publication Type:Journal Article
- Keywords:
postpartum hemorrhage;
placenta accreta;
deep learning;
radiomics;
blood loss
- From:
Chinese Journal of Medical Physics
2025;42(11):1523-1531
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a predictive model integrating clinical features,deep learning(DL),and radiomics based on T2-weighted imaging for prenatal assessment of postpartum hemorrhage(PPH)risk in high-risk pregnant women.Methods A total of 538 pregnant women with ultrasound-reported high-risk placenta accrete were retrospectively enrolled and divided into training,internal test,and external test cohorts.A nnUNet model was trained for automatic placental segmentation.Univariate and multivariate analyses were conducted on clinical features to identify those associated with PPH.Quantitative radiomic features were extracted from the placental region,and a random forest model was developed to predict estimated blood loss(EBL)and PPH risk.A DenseNet-based multi-task DL model was trained to predict PPH risk,EBL,and placenta previa status.Finally,a DL-radiomics ensemble(DRE)model was constructed by integrating clinical features,DL outputs,and radiomics scores.Diagnostic performance was evaluated using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results The DRE model achieved AUC values of 0.874(95%CI:0.792-0.951)and 0.836(95%CI:0.648-0.974)in the internal and external test cohorts,respectively,significantly outperforming the standalone clinical,DL,and radiomics models.Incorporation of EBL regression improved the performance of the PPH classification model,with the external test AUC increasing from 0.261-0.788 to 0.836.Conclusion The DRE model integrating DL and radiomics can efficiently predict PPH risk and assist in the clinical management of high-risk pregnancies.
