Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning
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.
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.
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.
• 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.
KeywordsMagnetic resonance imaging Placenta accreta Radiomics Computer-assisted image analysis Machine learning
Area under the ROC curve
Balanced turbo field echo
Laplacian of Gaussian
Simple placenta previa
Turbo spin echo
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).
Compliance with ethical standards
The scientific guarantor of this publication is S. Zhou.
Conflict of interest
The authors declare that they have no conflict of interest.
Statistics and biometry
One of the authors (H. Sun) has significant statistical expertise.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• cross-sectional study
• performed at one institution
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