Skip to main content

Advertisement

SpringerLink
Log in

Prediction of placenta accreta spectrum by combining deep learning and radiomics using T2WI: a multicenter study

  • Pelvis
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

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.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Abbreviations

PAS:

Placenta accreta spectrum

T2WI:

T2-weighted images

MRI:

Magnetic resonance imaging

CI:

Confidence interval

ROI:

Regions of interest

SVM:

Support vector machine

LASSO:

Least absolute shrinkage and selection operator

ROC:

Receiver operating characteristics

AUC:

Area under curve

References

  1. Shamshirsaz AA, Fox KA, Erfani H, et al (2017) Multidisciplinary team learning in the management of the morbidly adherent placenta: outcome improvements overtime. Am Journal Obstet Gynecol 216: 612.e1-612.e5. https://doi.org/10.1016/j.ajog.2017.02.016

    Article  Google Scholar 

  2. Solomon CG, Silver RM, Branch DW (2018) Placenta accreta spectrum. New Engl J Med 378: 1529-1536. https://doi.org/10.1056/NEJMcp1709324

    Article  Google Scholar 

  3. Ueno Y, Kitajima K, Kawakami F, et al (2014) Novel MRI finding for diagnosis of invasive placenta praevia: evaluation of findings for 65 patients using clinical and histopathological correlations. Eur Radiol 24: 881-888. https://doi.org/10.1007/s00330-013-3076-7

    Article  PubMed  Google Scholar 

  4. Zeng C, Yang M, Ding Y, et al (2018). Placenta accreta spectrum disorder trends in the context of the universal two‐child policy in China and the risk of hysterectomy. Int J Gynecol Obstet 140: 312-318. https://doi.org/10.1002/ijgo.12418

    Article  Google Scholar 

  5. Jauniaux E, Chantraine F, Silver RM, et al (2018) FIGO consensus guidelines on placenta accreta spectrum disorders: Epidemiology. Int J Gynecol Obstet 140: 265-273. https://doi.org/10.1002/ijgo.12407

    Article  Google Scholar 

  6. Kapoor H, Hanaoka M, Dawkins A, et al (2021) Review of MRI imaging for placenta accreta spectrum: pathophysiologic insights, imaging signs, and recent developments. Placenta 104: 31-39. https://doi.org/10.1016/j.placenta.2020.11.004

    Article  PubMed  Google Scholar 

  7. Jauniaux E, Collins S, Burton GJ (2017) Placenta accreta spectrum: pathophysiology and evidence-based anatomy for prenatal ultrasound imaging. Am J Obstet Gyneecol 218: 75-87. https://doi.org/10.1016/j.ajog.2017.05.067

    Article  Google Scholar 

  8. Do QN, Lewis MA, Yin X, et al (2020) MRI of the Placenta Accreta Spectrum (PAS) Disorder: Radiomics Analysis Correlates With Surgical and Pathological Outcome. J Magn Reson Imging 51: 936-946. https://doi.org/10.1002/jmri.26883

    Article  Google Scholar 

  9. Pizzi AD, Tavoletta A, Narciso R, et al (2019) Prenatal planning of placenta previa: diagnostic accuracy of a novel MRI-based prediction model for placenta accreta spectrum (PAS) and clinical outcome. Abdominal Radiology 44:1873-1882. https://doi.org/10.1007/s00261-018-1882-8

    Article  Google Scholar 

  10. Chen T, Xu X Q, Shi H B, et al (2017) Conventional MRI features for predicting the clinical outcome of patients with invasive placenta. Diagn Interv Radiol 23: 173. https://doi.org/10.5152/dir.2016.16412

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ueno Y, Maeda T, Tanaka U, et al (2016) Evaluation of interobserver variability and diagnostic performance of developed MRI‐based radiological scoring system for invasive placenta previa. J Magn Reson Imaging 44: 573-583. https://doi.org/10.1002/jmri.25184

    Article  PubMed  Google Scholar 

  12. Siauve N (2019) How and why should the radiologist look at the placenta? Eur Radiol 29: 6149-6151. https://doi.org/10.1007/s00330-019-06373-8

    Article  CAS  PubMed  Google Scholar 

  13. Wu Q, Yao K, Liu Z, et al (2019) Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: a multicentre study. EBioMedicine, 50: 355-365. https://doi.org/10.1016/j.ebiom.2019.11.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Romeo V, Ricciardi C, Cuocolo R, et al (2019) Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa. Magn Resone Imaging 64: 71-76. https://doi.org/10.1016/j.mri.2019.05.017

    Article  Google Scholar 

  15. Sun H, Qu H, Chen L, et al (2019) Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning. Eur Radiol 29: 6152-6162. https://doi.org/10.1007/s00330-019-06372-9

    Article  PubMed  Google Scholar 

  16. Ren H, Mori N, Mugikura S, et al (2021) Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging. Abdominal Radiology 46: 5344-5352. https://doi.org/10.1007/s00261-021-03226-1

    Article  PubMed  Google Scholar 

  17. Huang B, Tian J, Zhang H, et al (2020) Deep semantic segmentation feature-based radiomics for the classification tasks in medical image analysis. IEEE Journal of Biomedical and Health Informatics 25: 2655-2664. https://doi.org/10.1109/JBHI.2020.3043236

    Article  Google Scholar 

  18. Wei J, Cheng J, Gu D, et al (2021) Deep learning‐based radiomics predicts response to chemotherapy in colorectal liver metastases. Med Phys 48: 513-522. https://doi.org/10.1002/mp.14563

    Article  CAS  PubMed  Google Scholar 

  19. Song J, Ding C, Huang Q, et al (2021) Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma. Med Phys 48: 7891-7899. https://doi.org/10.1002/mp.15307

    Article  CAS  PubMed  Google Scholar 

  20. Xuan R, Li T, Wang Y, et al (2021) Prenatal prediction and typing of placental invasion using MRI deep and radiomic features. BioMedical Engineering OnLine 20: 1-18. https://doi.org/10.1186/s12938-021-00893-5

    Article  Google Scholar 

  21. Shao Q, Xuan R, Wang Y, et al (2021) Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI. Math Biosci Eng 18: 6198-6215. https://doi.org/10.3934/mbe.2021310

    Article  PubMed  Google Scholar 

  22. Li C, Qin Y, Zhang W, et al (2022) Deep learning‐based AI model for signet‐ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer. Med phys 49: 1535-1546. https://doi.org/10.1002/mp.15437

    Article  PubMed  Google Scholar 

  23. Wang S, Dong D, Li L, et al (2021) A deep learning radiomics model to identify poor outcome in COVID-19 patients with underlying health conditions: a multicenter study. IEEE Journal of Biomedical and Health Informatics 25: 2353-2362. https://doi.org/10.1109/JBHI.2021.3076086

    Article  PubMed  Google Scholar 

  24. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, pp. 234-241

    Google Scholar 

  25. He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778

  26. Perkinsm NJ, Schisterman EF (2006) The inconsistency of “optimal” cutpoints obtained using two criteria based on the receiver operating characteristic curve. American journal of epidemiology 163: 670-675. https://doi.org/10.1093/aje/kwj292

    Article  Google Scholar 

  27. Imafuku H, Tanimura K, Shi Y, et al (2021) Clinical factors associated with a placenta accreta spectrum. Placenta 112: 180-184. https://doi.org/10.1016/j.placenta.2021.08.001

    Article  PubMed  Google Scholar 

Download references

Funding

This work was supported in part by the Natural Science Foundation of Zhejiang Province under Grant no. LY20H180003, the Public Welfare Science and Technology Project of Ningbo under Grant no. 2022S043.

Author information

Author notes

  1. Zhengjie Ye and Rongrong Xuan contributed equally to this work.

Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China

    Zhengjie Ye & Wei Jin

  2. Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China

    Rongrong Xuan, Menglin Ouyang & Yutao Wang

  3. Ningbo Women’s and Children’s Hospital, Ningbo, 315012, China

    Jian Xu

Authors
  1. Zhengjie Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rongrong Xuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Menglin Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yutao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jian Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wei Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Jin.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 21 kb)

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, Z., Xuan, R., Ouyang, M. et al. Prediction of placenta accreta spectrum by combining deep learning and radiomics using T2WI: a multicenter study. Abdom Radiol 47, 4205–4218 (2022). https://doi.org/10.1007/s00261-022-03673-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-022-03673-4

Keywords

Search

Navigation