Predicting placenta accreta spectrum and high postpartum hemorrhage risk using radiomics from T2-weighted MRI

Abstract

Background: Antenatal diagnosis of placenta accreta spectrum (PAS) is of critical importance, considering that women have much better outcomes when delivery occurs at a level III or IV maternal care facility before labor initiation or bleeding, thus avoiding placental disruption. Herein, we aimed to investigate the performance of magnetic resonance imaging (MRI) in antenatal prediction of PAS and postpartum hemorrhage (PPH). Methods: This retrospective study included 433 women with singleton pregnancies (PAS group, n = 208; non-PAS group, n = 225; PPH-positive (PPH (+)) group, n = 80; PPH-negative (PPH (-)) group, n = 353), who were randomly divided into a training set and a test set in a 7:3 ratio. Radiomic features were extracted from T2WI (T2-weighted imaging). Features strongly correlated with PAS and PPH (p < 0.05) were selected using Pearson correlation, followed by LASSO regression for dimensionality reduction. Subsequently, radiomics models were constructed for PAS and PPH risk prediction, respectively. Regression analyses were conducted using radiomics score (R-score) and clinical factors to identify independent clinical risk factors for PAS and PPH, leading to the development of corresponding clinical models. Next, clinical-radiomics models were built by combining R-score and clinical risk factors. The predictive performance of the models was evaluated using nomograms, calibration curves, and decision curves. Results: The clinical-radiomics models and radiomics models for predicting PAS and PPH risk both outperformed their clinical models in the training and testing sets. For PAS, the AUC (Area Under the Receiver Operating Characteristic Curve) of the clinical-radiomics model, radiomics model, and clinical model in the training set are 0.918, 0.908, and 0.755, respectively, and in the testing set, the AUCs are 0.885, 0.866, and 0.771, respectively. For PPH, the AUCs of the clinical-radiomics model, radiomics model, and clinical model in the training set are 0.918, 0.884, and 0.723, respectively, and in the testing set, the AUCs are 0.905, 0.860, and 0.688, respectively. The DeLong test p-values between the clinical-radiomics models and radiomics models for predicting PAS and PPH are both less than 0.05. Additionally, in the testing set, the clinical-radiomics models perform best in predicting PAS and PPH risk, with accuracies of 82.31% and 84.61%, respectively. Conclusion: This novel clinical-radiomics model has a robust performance in predicting PAS antepartum and predicting massive PPH in pregnancies.