The value of T2WI imaging-based histology in the ability to identify penetrating placenta implantation

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• Clinical Article •

FENG Liujuan ZHANG Lingjie CHENG Meiying ZHANG Xiaoan LI Sike LU Yu LIU Shipeng YANG Jinze ZHAO Xin

Cite this article as: FENG L J, ZHANG L J, CHENG M Y, et al. The value of T2WI imaging-based histology in the ability to identify penetrating placenta implantation[J]. Chin J Magn Reson Imaging, 2025, 16(3): 83-89. DOI:10.12015/issn.1674-8034.2025.03.013.

Abstract

[Abstract] Objective To explore the ability of a MRI based imaging histologic model to identify placenta percreta (PP).Materials and Methods A retrospective study was conducted to collecting data from 80 cases of pregnant women who underwent placental MRI scanning and MRI indications pointing to PP in the Department of Radiology of the Third Affiliated Hospital of Zhengzhou University from January 2021 to December 2023, with surgical findings as the standard, including 48 cases of PP and 32 cases of non-PP. The region of interest was manually outlined on the axial, coronal and sagittal T2WI sequences, and the features of imaging histology were extracted. All patients were randomly divided into training and test sets in the ratio of 7∶3. The extracted imaging histology features were firstly subjected to Z-score regularization, then feature screening by t test, followed by calculation of Pearson correlation coefficients, and finally the least absolute shrinkage and selection operator algorithm was used. Selection operator algorithm for screening and dimensionality reduction of the features of the histology, and calculate the radiomics score. The optimal algorithm was selected from 7 different machine learning algorithms and used to construct an radiomics model. Univariate logistic regression analysis was performed on both clinical data and radiomics scores, revealing statistically significant differences. Subsequently, factors demonstrating significant differences were incorporated into multivariate analysis to identify independent risk factors (clinical information was used to construct clinical models). These factors were then visualized to construct a predictive combined model (nomogram). The receiver operating characteristic curve was plotted, and the efficacy of the model was compared by the indicators of area under the curve (AUC), sensitivity, specificity, and accuracy, and the calibration curve was used to evaluate the calibration degree of the model, and the decision curve analysis was used to assess the effectiveness of the model. The calibration curve was used to evaluate the calibration degree of the model, and the decision curve analysis was used to assess the clinical utility value of the model.Results The multivariate analysis identified two independent risk factors: parity and radiomics score. Parity demonstrated a protective effect with an odds ratio of 0.272 [95% confidence interval (CI): 0.151 to 0.492], while the radiomics score showed a strong positive association with an exceptionally high odds ratio of 1 934.105 (95% CI: 118.985 to 31 445.149). The AUC values for the imaging histology model and the clinical model in the training set were 0.948 (95% CI: 0.884 to 1.000) and 0.723 (95% CI: 0.596 to 0.850), respectively, and in the test set were 0.828 (95% CI: 0.601 to 1.000) and 0.676 (95% CI: 0.474 to 0.878). The AUC value of the imaging histology-clinical model in the training set was 0.962 (95% CI: 0.906 to 1.000). The results of DeLong test showed that there were significant differences in the training set, both between the clinical model and the imaging histology model as well as between the clinical model and the imaging histology-clinical model (P < 0.05), but the differences between the imaging histology model and the imaging histology-clinical model were not statistically significant (P > 0.05). Both the radiomics model and the radiomics-clinical model had good calibration and clinical application value in the test set.Conclusions Imaging histology-clinical modeling has better diagnostic efficacy and can be used as a modality for the identification of PP. It provides a reliable foundation for clinicians in determining the timing and method of pregnancy termination, thereby aiding in the formulation of informed clinical decisions.

[Keywords] placenta accreta spectrum disorders；placenta percreta；radiomics；magnetic resonance imaging；differentiate

Contributor Information

FENG Liujuan¹ ZHANG Lingjie² CHENG Meiying¹ ZHANG Xiaoan¹ LI Sike¹ LU Yu¹ LIU Shipeng¹ YANG Jinze¹ ZHAO Xin^1*

¹ Department of Medical Imaging, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China

² Department of Ultrasound Medicine, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China

Corresponding author: ZHAO X, E-mail: zdsfyzx@zzu.edu.cn

Conflicts of interest None.

Publication Information

Received 2025-01-13

Accepted 2025-03-10

DOI: 10.12015/issn.1674-8034.2025.03.013

Text

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