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Objective <p>To develop and validate a radiomics-clinical model of MRI fusion model for the prenatal prediction of placenta accreta spectrum (PAS) and pregnancy outcomes.</p> Methods <p>This multicentre retrospective study enrolled 610 singleton pregnancies (292 with PAS vs.318 controls). After stratified randomization, the participants were allocated to training (<i>n</i> = 408), validation (<i>n</i> = 102), or external testing (<i>n</i> = 100) cohorts. Radiomics features were extracted from T2-weighted sagittal images, and MRI radiomics models, prenatal clinical models, and combined prediction models were developed.</p> Results <p>Prenatal clinical characteristics were significantly correlated with the presence of PAS in the training cohort (<i>p</i> &lt; 0.05, top = 0.001). The newly constructed integrated prediction model incorporating radiomics features and the three prenatal clinical features had a greater area under the curve (AUC) for predicting PAS than the MRI radiomics and prenatal clinical models in the training cohort (0.767, 0.764, and 0.644; <i>p</i> &lt; 0.05), validation cohort (0.795, 0.759, and 0.708; <i>p</i> &lt; 0.05), and external testing cohort (0.792, 0.777, and 0.758; <i>p</i> &lt; 0.05). Furthermore, in terms of pregnancy outcomes in pregnant women with PAS, the combined prediction model had a greater AUC than the MRI radiomics and prenatal clinical models in the training cohort (0.852, 0.834, and 0.673; <i>p</i> &lt; 0.05), the validation cohort (0.879, 0.804, and 0.747; <i>p</i> &lt; 0.05), and the external testing cohort (0.907, 0.839, and 0.700; <i>p</i> &lt; 0.05).</p> Conclusion <p>The MRI-based multimodal integrated framework significantly outperforms conventional models, achieving dual prediction of PAS diagnosis and pregnancy outcomes with clinical-grade accuracy.</p> Critical relevance statement <p>Our study introduces the models provide objective and efficient decision support for the management of pregnant women with high-risk placenta previa and are expected to become a core tool for the multidisciplinary management of PAS.</p>

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Development and validation of a combined model based on MRI radiomics for predicting placenta accreta spectrum and adverse pregnancy outcomes: a multicenter retrospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月15日

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  • Zhongjie Zhou,
  • Huaizhi Ge,
  • Pinfa Zou,
  • Tianyi Ye,
  • Ting Zou,
  • Qin Fang,
  • Yan Zeng,
  • Yu Wang,
  • Xiaofen Zhang,
  • Zhangye Xu,
  • Xianping Huang

摘要

Objective

To develop and validate a radiomics-clinical model of MRI fusion model for the prenatal prediction of placenta accreta spectrum (PAS) and pregnancy outcomes.

Methods

This multicentre retrospective study enrolled 610 singleton pregnancies (292 with PAS vs.318 controls). After stratified randomization, the participants were allocated to training (n = 408), validation (n = 102), or external testing (n = 100) cohorts. Radiomics features were extracted from T2-weighted sagittal images, and MRI radiomics models, prenatal clinical models, and combined prediction models were developed.

Results

Prenatal clinical characteristics were significantly correlated with the presence of PAS in the training cohort (p < 0.05, top = 0.001). The newly constructed integrated prediction model incorporating radiomics features and the three prenatal clinical features had a greater area under the curve (AUC) for predicting PAS than the MRI radiomics and prenatal clinical models in the training cohort (0.767, 0.764, and 0.644; p < 0.05), validation cohort (0.795, 0.759, and 0.708; p < 0.05), and external testing cohort (0.792, 0.777, and 0.758; p < 0.05). Furthermore, in terms of pregnancy outcomes in pregnant women with PAS, the combined prediction model had a greater AUC than the MRI radiomics and prenatal clinical models in the training cohort (0.852, 0.834, and 0.673; p < 0.05), the validation cohort (0.879, 0.804, and 0.747; p < 0.05), and the external testing cohort (0.907, 0.839, and 0.700; p < 0.05).

Conclusion

The MRI-based multimodal integrated framework significantly outperforms conventional models, achieving dual prediction of PAS diagnosis and pregnancy outcomes with clinical-grade accuracy.

Critical relevance statement

Our study introduces the models provide objective and efficient decision support for the management of pregnant women with high-risk placenta previa and are expected to become a core tool for the multidisciplinary management of PAS.