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Cited by 5Maternal and fetal healthcareRadiomics and Machine Learning in Medical ImagingPregnancy and preeclampsia studies

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

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Zou, Jinli, Wei, Wei, Xiao, Yingzhen, Wang, Xinlian, Wang, Keyang, Xie, Lizhi, Liang, Yuting·2025·BMC Pregnancy and Childbirth
DOI 10.1186/s12884-025-07516-0PMC PMC11971782
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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.

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  • The integration of deep learning techniques consistently outperformed manual diagnostic methods for PAS when trained on quality datasets.

    “The integration of deep learning techniques consistently outperformed manual diagnostic methods when trained on quality datasets [24-26].”

    From Machine Learning in Maternity: Early Diagnosis of Placenta Accreta Spectrum (2026) · cites this paper as [24]

    The Results (Model performance assessment; page not provided) show that radiomics and clinical-radiomics models outperformed a clinical model for PAS in both training and test sets. But the Methods (Models construction; page not provided) identify logistic regression, not deep learning, and the paper does not compare its models with manual diagnosis or establish that performance depends on dataset quality. (LLM, conf=0.82)

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Research works printed but not linked: 1

  1. [1]Brown LA, Menendez-bobseine M. Placenta accreta spectrum. J Midwifery Women’s Health. 2021;66(2):265–9. 10.1111/jmwh. 33338302 · DOI

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Authors
Zou, J., Wei, W., Xiao, Y., Wang, X., Wang, K., Xie, L., & Liang, Y.
Affiliations
  • Capital Medical University (CN)
  • Xi'an Polytechnic University (CN)
Corresponding author
Liang, Yuting
ORCID
Venue
BMC Pregnancy and Childbirth
Publisher
Springer Science and Business Media LLC
Published
2025-04-04
Volume
25
Issue
1
Pages
398-398
ISSN
1471-2393 (ISSN-L)
DOI
10.1186/s12884-025-07516-0
PMC
PMC11971782
OpenAlex
W4409151127
Cited by
5
References
37
Citations by year
2026: 5
FWCI
7.54
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Pregnancy and Childbirth (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Natural Science Basic Research Program of Shanxi (2023-JC-YB-682)
  • National Natural Science Foundation of China (82371936)
  • Capital’s Funds for Health Improvement and Research (2022-2-2117)
Topics
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Maternal and fetal healthcare
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Obstetrics and Gynecology › Pregnancy and preeclampsia studies
Keywords
placenta accreta spectrum, postpartum hemorrhage, radiomics, T2-weighted MRI, prenatal prediction, risk prediction model, clinical-radiomics model, nomogram, singleton pregnancy, LASSO, clinical risk factors, decision curve analysis, calibration curve
MeSH terms
Radiomics, Adult, Female, Postpartum Hemorrhage, Humans, Magnetic Resonance Imaging, Placenta Accreta, Predictive Value of Tests, Pregnancy, Prenatal Diagnosis, Retrospective Studies, Risk Factors, ROC Curve, Risk Assessment

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