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MRI–radiomics–clinical–based nomogram for prenatal prediction of the placenta accreta spectrum disorders

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Abstract

Objectives

To investigate whether an MRI–radiomics–clinical–based nomogram can be used to prenatal predict the placenta accreta spectrum (PAS) disorders.

Methods

The pelvic MR images and clinical data of 156 pregnant women with pathologic-proved PAS (PAS group) and 115 pregnant women with no PAS (non-PAS group) identified by clinical and prenatal ultrasonic examination were retrospectively collected from two centers. These pregnancies were divided into a training (n = 133), an independent validation (n = 57), and an external validation (n = 81) cohort. Radiomic features were extracted from images of transverse oblique T2-weighted imaging. A radiomics signature was constructed. A nomogram, composed of MRI morphological findings, radiomic features, and prenatal clinical characteristics, was developed. The discrimination and calibration of the nomogram were conducted to assess its performance.

Results

A radiomics signature, including three PAS–related features, was associated with the presence of PAS in the three cohorts (p < 0.001 to p = 0.001). An MRI–radiomics–clinical nomogram incorporating radiomics signature, two prenatal clinical features, and two MRI morphological findings was developed, yielding a higher area under the curve (AUC) than that of the MRI morphological-determined PAS in the training cohort (0.89 vs. 0.78; p < 0.001) and external validation cohort (0.87 vs. 0.75; p = 0.003), while a comparable AUC value in the validation cohort (0.91 vs. 0.81; p = 0.09). The calibration was good.

Conclusions

An MRI–radiomics–clinical nomogram had a robust performance in antenatal predicting the PAS in pregnancies.

Key Points

• An MRI–radiomics–clinical–based nomogram might serve as an adjunctive approach for the treatment decision-making in pregnancies suspicious of PAS.

• The radiomic score provides a mathematical formula that predicts the possibility of PAS by using the MRI data, and pregnant women with PAS had higher radiomic scores than those without PAS.

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Abbreviations

AUC:

Area under the curve

DICOM:

Digital imaging and communications in medicine

ICC:

Intra- and inter-class correlation coefficients

IDI:

Integrated discrimination improvement

LASSO:

Least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

NRI:

Net reclassification improvement

PAS:

Placenta accreta spectrum

ROC:

Receiver operating characteristic

T1WI:

T1-weighted imaging

T2WI:

T2-weighted imaging

VIF:

Variance inflation factor

VOI:

Volume of interest

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Acknowledgements

We thank Wen Sun, M.D. (Department of Gynaecology and Obstetrics, The Third Affiliated Hospital of Guangzhou Medical University) and Qian Gao, M.D. (Department of Gynaecology and Obstetrics, The Third Affiliated Hospital of Sun Yat-Sen University) for their kind help with the clinical issue consultation.

Funding

This work was supported by the Natural Science Foundation of Guangdong Province (2021A1515010385) and the Medical Artificial Intelligence Project of Sun Yat-Sen Memorial Hospital (YXRGZN201905).

Author information

Author notes

  1. Lulu Peng and Xiang Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, No. 63 Duobao Road, Guangzhou, 510000, People’s Republic of China

    Lulu Peng, Jue Liu, Yi Liu, Jianwei Huang & Ting Song

  2. Guangzhou Institute of Obstetrics & Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, No. 63 Duobao Road, Guangzhou, 510000, People’s Republic of China

    Lulu Peng, Jue Liu, Yi Liu, Jianwei Huang & Ting Song

  3. Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, No. 63 Duobao Road, Guangzhou, 510000, People’s Republic of China

    Lulu Peng, Jue Liu, Yi Liu, Jianwei Huang & Ting Song

  4. Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People’s Republic of China

    Xiang Zhang, Junwei Chen, Yun Su & Zehong Yang

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  1. Lulu Peng
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Corresponding authors

Correspondence to Zehong Yang or Ting Song.

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Guarantor

The scientific guarantor of this publication is Ting Song.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained from the Institutional Review Board of the Third Affiliated Hospital of Guangzhou Medical University (Guangzhou, China) (2022-006) and Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University (Guangzhou, China) (SYSEC-KY-KS-2022-104).

Methodology

• retrospective

• diagnostic study

• two-center study

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Peng, L., Zhang, X., Liu, J. et al. MRI–radiomics–clinical–based nomogram for prenatal prediction of the placenta accreta spectrum disorders. Eur Radiol 32, 7532–7543 (2022). https://doi.org/10.1007/s00330-022-08821-4

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  • DOI: https://doi.org/10.1007/s00330-022-08821-4

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