Prediction of Placenta Accreta Spectrum by MRI Imaging Based on Deep Learning
- VernacularTitle:基于深度学习的磁共振成像影像组学预测胎盘植入性疾病
- Author:
Xiao LING
1
;
Yurui HU
;
Yingchao WANG
Author Information
1. 兰州大学第二医院(第二临床医学院)核磁共振科,甘肃 兰州 730030
- Publication Type:Journal Article
- Keywords:
Magnetic resonance imaging;
Deep learning;
Placenta accreta spectrum;
Radiomics
- From:
Journal of Practical Obstetrics and Gynecology
2025;41(3):230-236
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of deep learning imageomics based on MRI sagittal T2WI images in predicting placenta accreta spectrum in high-risk pregnant women.Methods:The complete data of 265 pregnant women who underwent MRI due to suspected placenta implantation in The Second Hospital&Clinical Medical School,Lanzhou University and Zhangye People's Hospital Affiliated to Hexi University from January 2019 to De-cember 2023 were analyzed retrospectively.The patients were randomly divided into training group(n=172)and validation group(n=93)at 7∶3.Multivariate Logistic regression analysis was used to screen the independent risk factors among clinical and imaging characteristics.Radiomics features were extracted based on sagittal T2WI images.Using the DenseNet-121 model as the basic model for deep learning feature extraction,traditional clinical model,radiomic model and deep learning model were constructed to predict PAS.The diagnostic efficiency of each model was evaluated by the area under the receiver operating characteristic(ROC)curve(AUC).Finally,the model with the highest performance was determined as the optimal model.Results:In both the training and validation groups,the PAS group and normal group exhibited statistically significant differences(P<0.05)in terms of the number of cesarean section≥2,history of placenta previa,and placental thickness>40 mm.Multivariate Logistic regression analysis revealed that cesarean section history,placental thickness and placenta previa were independent risk factors for predicting PAS.Among all the models constructed,the diagnostic performance of the combination model of deep learning combined with clinic was higher than the other three models.The AUC in training group and verification group were 0.96(95%CI 0.93-0.98)and 0.91(95%CI 0.87-0.95)respectively.Conclusions:The combined clinical model of deep learning based on MRI may have better performance in the di-agnosis of PAS than clinical or traditional radiomic models.
