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Abstract #3703

An automatic pipeline combining deep learning and radiomics to predict placenta accrete spectrum disorders from T2W MR images

Haijie Wang1, Yida Wang1, Lei Ling2, Jue Wang3, Xiaotian Li3, Hao Zhu3, He Zhang2, and Guang Yang1
1Shanghai key lab of magnetic resonance, East China Normal University, Shanghai, China, 2Department of Radiology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China, 3Department of Obstetrics, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China


Placenta accrete spectrum (PAS) disorders may lead to common complication like catastrophic perinatal hemorrhage. T2W is the most useful MRI sequence for identification of PAS disorders, but the diagnosis of PAS is often difficult and highly subjective. To overcome this problem, we automatically segmented the placental regions by nnU-Net and used radiomics features extracted from the segmented region to build a radiomics-clinical model for identification of placenta invasion. 512 pregnant women were enrolled in this study. Our segmentation model achieved a mean Dice coefficient of 0.890 and the classification model achieved an AUC of 0.849 on the independent validation cohort.

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