Placenta · 2025
Radiomics analysis of placental MRI for prenatal prediction of placenta accreta spectrum in pregnant women in the third trimester: A retrospective study of 594 patients.
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Li Kui, Yan Guohui, Zhang Xiaodan, Kong Jianchun, Zou Yu, Cheng Xiaodong
Аннотация
OBJECTIVE: To develop and validate a model based on placental MRI for the prenatal prediction of placenta accreta spectrum (PAS) in pregnant women in the third trimester.
MATERIALS AND METHODS: A total of 594 pregnant women who were suspected of having PAS and underwent placental MRI antenatally were included and were allocated into the training cohort and testing cohort at a 2:1 ratio. MRI diagnosis was determined by three experienced radiologists. Radiomic features were extracted from images of T2 weighted imaging for each patient. After a feature selection strategy, a radiomics signature and a clinical-radiomics nomogram combining radiomics score and clinical risk factors were constructed to predict PAS. The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and clinical utility.
RESULTS: MRI diagnosis yielded AUCs of 0.77 and 0.79 for predicting PAS in the training and testing cohorts, respectively. The AUCs of the radiomics signature used to predict PAS in both cohorts were 0.80 and 0.83, respectively. The nomogram accurately predicted PAS in both cohorts (AUC = 0.84 and 0.89), with better results than those of MRI diagnosis and radiomics signature in the training (p = 0.009 and 0.003, respectively) and testing cohorts (p = 0.010 and 0.008, respectively), decision curve analysis confirmed its best clinical utility compared to the other models.
CONCLUSION: Radiomics analysis based on placental MRI may serve as an effective tool to predict PAS in patients with possible PAS in the third trimester.
MATERIALS AND METHODS: A total of 594 pregnant women who were suspected of having PAS and underwent placental MRI antenatally were included and were allocated into the training cohort and testing cohort at a 2:1 ratio. MRI diagnosis was determined by three experienced radiologists. Radiomic features were extracted from images of T2 weighted imaging for each patient. After a feature selection strategy, a radiomics signature and a clinical-radiomics nomogram combining radiomics score and clinical risk factors were constructed to predict PAS. The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and clinical utility.
RESULTS: MRI diagnosis yielded AUCs of 0.77 and 0.79 for predicting PAS in the training and testing cohorts, respectively. The AUCs of the radiomics signature used to predict PAS in both cohorts were 0.80 and 0.83, respectively. The nomogram accurately predicted PAS in both cohorts (AUC = 0.84 and 0.89), with better results than those of MRI diagnosis and radiomics signature in the training (p = 0.009 and 0.003, respectively) and testing cohorts (p = 0.010 and 0.008, respectively), decision curve analysis confirmed its best clinical utility compared to the other models.
CONCLUSION: Radiomics analysis based on placental MRI may serve as an effective tool to predict PAS in patients with possible PAS in the third trimester.