Prediction of placenta accreta spectrum by combining deep learning and radiomics using T2WI: a multicenter study
Abstract
Purpose To achieve prenatal prediction of placenta accreta spectrum (PAS) by combining clinical model, radiomics model, and deep learning model using T2-weighted images (T2WI), and to objectively evaluate the performance of the prediction through multicenter validation.Methods A total of 407 pregnant women from two centers undergoing preoperative magnetic resonance imaging (MRI) were retrospectively recruited. The patients from institution I were divided into a training cohort (n = 298) and a validation cohort (n = 75), while patients from institution II served as the external test cohort (n = 34). In this study, we built a clinical prediction model using patient clinical data, a radiomics model based on selected key features, and a deep learning model by mining deep semantic features. Based on this, we developed a combined model by ensembling the prediction results of the three models mentioned above to achieve prenatal prediction of PAS. The performance of these predictive models was evaluated with respect to discrimination, calibration, and clinical usefulness.Results The combined model achieved AUCs of 0.872 (95% confidence interval, 0.843 to 0.908) in the validation cohort and 0.857 (0.808 to 0.894) in the external test cohort, both of which outperformed the other models. The calibration curves demonstrated excellent consistency in the validation cohort and the external test cohort, and the decision curves indicated high clinical usefulness.Conclusion By using preoperative clinical information and MRI images, the combined model can accurately predict PAS by ensembling clinical model, radiomics model, and deep learning model.
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In a 2022 multicenter study by Ye et al., the ensemble approach demonstrated strong classification performance and consistent stability across multiple centers for PAS diagnosis using MRI.
“Overall, the ensemble approach demonstrated strong classification performance and consistent stability across multiple centers [30].”
From Machine Learning in Maternity: Early Diagnosis of Placenta Accreta Spectrum (2026) · cites this paper as [30]
In one of the largest included studies, over 70% of PAS cases had neither prior Caesarean section nor placenta previa, the two most important and frequent predisposing risk factors.
“In one of the largest studies included, over 70% of PAS cases had neither of the two most important and frequent predisposing risk factors [16], which are a prior Caesarean section and placenta previa [17].”
From Radiomics-based prediction of FIGO grade for placenta accreta spectrum (2023) · cites this paper as [16]
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- Authors
- Ye, Z., Xuan, R., Ouyang, M., Wang, Y., Xu, J., & Jin, W.
- Affiliations
- Ningbo University (CN)
- Corresponding author
- Jin, Wei
- Venue
- Abdominal Radiology
- Published
- 2022-09-12
- Volume
- 47
- Issue
- 12
- Pages
- 4205-4218
- ISSN
- 2366-004X (ISSN-L)
- 2366-0058
- OpenAlex
- W4295232624
- Cited by
- 24
- References
- 26
- Citations by year
- 2026: 6 · 2025: 2 · 2024: 8 · 2023: 8
- FWCI
- 4.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Abdominal Radiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Natural Science Foundation of Zhejiang Province (LY20H180003)
- 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 › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- placenta accreta spectrum, prenatal prediction, T2-weighted MRI, radiomics, deep learning, ensemble prediction model, clinical prediction model, preoperative MRI, pregnancy complications
- MeSH terms
- Deep Learning, Female, Humans, Magnetic Resonance Imaging, Placenta Accreta, Pregnancy, Prognosis, Retrospective Studies, Models, Statistical
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