Association between deep learning radiomics based on placental MRI and preeclampsia with fetal growth restriction: A multicenter study.

PURPOSE: Preeclampsia (PE) is associated with placental insufficiency and could lead to adverse pregnancy outcomes. The study aimed to develop a placental T2-weighted image-based automatic quantitative model for the identification of PE pregnancies and disease severity.

METHODS

Between July 2013 and September 2022, the retrospective multicenter study featured 420 pregnant women, including 140 cases of PE and 280 cases of normotensive pregnancies. The semi-supervised approach was used to gain an automatic segmentation for placental MRI. The radiomics, deep learning, and deep learning radiomics (DLR) models were built.

RESULTS

In PE pregnancies, 65 (46.4 %) fetuses developed PE with fetal growth restriction (FGR), and 75 (53.6 %) cases were PE without FGR. The Dice of semi-supervised placental segmentation was 0.917. The AUCs of the DLR signature for discriminating PE pregnancies from normotensive pregnancies were 0.839 (95 % CI: 0.793-0.886), 0.858 (95 % CI: 0.742-0.974), 0.888 (95 % CI: 0.783-0.992), and 0.843 (95 % CI: 0.731-1.000) in the training, test, internal validation, and external validation sets, respectively. This DLR analysis model performed well in discriminating between PE with FGR and normotensive pregnancies (AUC = 0.918, 95 % CI: 0.879-0.957) and PE without FGR (AUC = 0.742, 95 % CI: 0659-0.824).

CONCLUSION

The automatic radiomics analysis has been developed to identify PE pregnancies by determining DLR features on placental T2-weighted images, and to predict FGR exposed to PE.