A machine learning model based on placental magnetic resonance imaging and clinical factors to predict fetal growth restriction.

Abstract

Objectives: To create a placental radiomics-clinical machine learning model to predict FGR.

Materials and methods: Retrospectively analyzed placental MRI and clinical data of 110 FGR cases and 158 healthy controls at 28-37 weeks of gestation from two campuses of ZWH. 227 cases from Hubin campus were randomly divided into training (n = 182) and internal testing set (n = 45). 41 cases from Xiaoshan campus were included in external testing set. Placental MRI features were extracted from sagittal T2WI. Mann-Whitney U test, redundancy analysis, and LASSO were used to identify the radiomics signature, and the best-performing radiomics model was constructed by comparing eight machine learning algorithms. Clinical factors determined by univariate and multivariate analyses. A united model and nomogram combining the radiomics Rad-score and clinical factors were established. The performance of the models was assessed by DeLong test, calibration curve and decision curve analysis.

Results: Of 1561 radiomics features, 10 strongly correlated with FGR were selected. The radiomics model using logistic regression performed best compared eight algorithms. 5 important clinical features identified by analysis. The united model demonstrated a good predictive performance in the training, internal testing and external testing sets, with AUC 0.941 (95% CI, 0.0.904-0.977), 0.899 (95% CI, 0.789-1) and 0.861 (95% CI 0.725-0.998), prediction accuracies 0.885, 0.844 and 0.805, precisions 0.871, 0.789 and 0.867, recalls 0.836, 0.833 and 0.684, and F1 scores 0.853, 0.811 and 0.765, respectively. The calibration and decision curves of the united model also showed good performance. Nomogram confirmed clinical applicability of the model.

Conclusions: The proposed placental radiomics-clinical machine learning model is simple yet effective to predict FGR.