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European Radiology

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01-11-2019 | Magnetic Resonance Imaging | Imaging Informatics and Artificial Intelligence

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

Authors: Huaiqiang Sun, Haibo Qu, Lu Chen, Wei Wang, Yi Liao, Ling Zou, Ziyi Zhou, Xiaodong Wang, Shu Zhou

Published in: European Radiology | Issue 11/2019

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.

Available only for authorised users

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Title: Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning
Authors: Huaiqiang Sun
Haibo Qu
Lu Chen
Wei Wang
Yi Liao
Ling Zou
Ziyi Zhou
Xiaodong Wang
Shu Zhou
Publication date: 01-11-2019
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Published in: European Radiology / Issue 11/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-019-06372-9

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Objective

Material and methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Material and methods

Results

Conclusions

Key Points

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