Abstract
BACKGROUND: Placenta accreta spectrum(PAS)is a major cause of maternal mortality during the perinatal period. This study aims to develop and validate a novel nomogram prediction model integrating clinical characteristics and ultrasound radiomics features for predicting PAS in pregnant women with placenta previa. METHODS: This retrospective two-center study included 271 pregnant women with placenta previa from two medical centers in China. Center 1 (n = 190) served as the training cohort, and Center 2 (n = 81) as the external validation cohort. Radiomic features were extracted from two-dimensional gray-scale ultrasound images of the placenta. Least absolute shrinkage and selection operators (LASSO)were used to select the radiomic features, and multivariate logistic regression identified independent clinical risk factors. A nomogram was constructed by incorporating selected radiomic features and significant clinical risk factors. Model performance was assessed using receiver operating characteristic (ROC) curve analysis (area under the curve, AUC) and decision curve analysis (DCA). RESULTS: The radiomics-only model achieved an AUC of 0.853 in the training cohort and 0.778 in the external validation cohort. The number of prior cesarean deliveries (CD) was identified as an independent risk factor and was therefore included in the clinical model, which was further integrated into a nomogram. The clinical-radiomics model, which combines radiomic features and clinical independent risk factors, achieved AUCs of 0.859 (training cohort) and 0.822 (external validation cohort). The integrated nomogram demonstrated slightly higher diagnostic performance than radiomics-only models (AUC 0.859 vs. 0.853 in the training cohort; 0.822 vs. 0.778 in the external validation cohort). DCA showed that the nomogram based on the clinical-radiomics model provided the highest clinical net benefit. CONCLUSIONS: The clinical-ultrasound radiomics nomogram has favorable predictive value for PAS in patients with placenta previa. This tool may assist clinicians in optimizing prenatal management and potentially improving maternal prognosis.