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Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

An Editorial Comment to this article was published on 07 August 2019

Abstract

Objective

The aim of this study was to investigate whether intraplacental texture features from routine placental MRI can objectively and accurately predict invasive placentation.

Material and methods

This retrospective study includes 99 pregnant women with pathologically confirmed placental invasion and 56 pregnant women with simple placenta previa. All participants underwent magnetic resonance imaging after 24 gestational weeks. The placenta was segmented in sagittal images from both turbo spin echo (TSE) and balanced turbo field echo (bTFE) sequences. Textural features were extracted from the both original and Laplacian of Gaussian (LoG)-filtered MRI images. An automated machine learning algorithm was applied to the extracted feature sets to obtain the optimal preprocessing steps, classification algorithm, and corresponding hyper-parameters.

Results

A gradient boosting classifier using all textual features from original and LoG-filtered TSE images and bTFE images identified by the automated machine learning algorithm achieved the optimal performance with sensitivity, specificity, accuracy, and area under ROC curve (AUC) of 100%, 88.5%, 95.2%, and 0.98 in the prediction of placental invasion. In addition, textural features that contributed to the prediction of placental invasion differ from the features significantly affected by normal placenta maturation.

Conclusions

Quantifying intraplacental heterogeneity using LoG filtration and texture analysis highlights the different heterogeneous appearance caused by abnormal placentation relative to normal maturation. The predictive model derived from automated machine learning yielded good performance, indicating the proposed radiomic analysis pipeline can accurately predict placental invasion and facilitate clinical decision-making for pregnant women with suspicious placental invasion.

Key Points

• The intraplacental texture features have high efficiency in prediction of invasive placentation after 24 gestational weeks.

• The features with dominated predictive power did not overlap with the features significantly affected by gestational age.

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Abbreviations

AUC:

Area under the ROC curve

bTFE:

Balanced turbo field echo

LoG:

Laplacian of Gaussian

PI:

Placental invasion

SPP:

Simple placenta previa

TSE:

Turbo spin echo

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Funding

This study was supported by the National Natural Science Foundation of China (81601458), the Ministry of Science and Technology of China (2016YFC0100803, 2018YFC1004603), the Health and Family Planning Commission of Sichuan Province (16ZD017), and Bureau of Science and Technology of Chengdu City (2014-HM01-00314-SF).

Author information

Author notes

  1. Huaiqiang Sun and Haibo Qu contributed equally to this work.

Authors and Affiliations

  1. Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China

    Huaiqiang Sun, Lu Chen & Ling Zou

  2. Department of Radiology, West China Second Hospital of Sichuan University, Chengdu, Sichuan, China

    Haibo Qu & Yi Liao

  3. Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China

    Haibo Qu, Wei Wang, Yi Liao, Ziyi Zhou, Xiaodong Wang & Shu Zhou

  4. Department of Periodical Press, West China Hospital of Sichuan University, Chengdu, Sichuan, China

    Lu Chen

  5. Department of Pathology, West China Second Hospital of Sichuan University, Chengdu, Sichuan, China

    Wei Wang

  6. Department of Obstetrics and Gynecology, West China Second Hospital of Sichuan University, Chengdu, 610041, Sichuan, China

    Ziyi Zhou, Xiaodong Wang & Shu Zhou

Authors
  1. Huaiqiang Sun
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  2. Haibo Qu
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  9. Shu Zhou
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Corresponding author

Correspondence to Shu Zhou.

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The scientific guarantor of this publication is S. Zhou.

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The authors declare that they have no conflict of interest.

Statistics and biometry

One of the authors (H. Sun) has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• retrospective

• cross-sectional study

• performed at one institution

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

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  • DOI: https://doi.org/10.1007/s00330-019-06372-9

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