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 mo

SUBMITTER: Wang J 

PROVIDER: S-EPMC11924743 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature