Diagnostic value of combining radiomics and clinical features in placenta accreta spectrum

Motivation: Placenta accrete spectrum disorder (PAS) is a dangerous pregnancy complication that posed a threat to the safety of pregnant women, and its incidence is still on the rise. Goal(s): To develop a machine learning model for effective diagnosis of PAS. Approach: We developed machine learning models based on T2WI radiomics, clinical features, and clinical-radiomics features in the diagnosis of PAS. Results: Radiomics models have a great diagnostic performance for PAS, with sagittal-based model shows better performance. The clinical-radiomics model exhibits the highest diagnostic performance in this study. Impact: Machine learning models that combined with radiomics and clinical features can improve the diagnosis of PAS, and benefit PAS patients. Furthermore, our results provide new insights for future research.