Radiomics-based prediction of FIGO grade for placenta accreta spectrum

Abstract

Abstract Background Placenta accreta spectrum (PAS) is a rare, life-threatening complication of pregnancy. Predicting PAS severity is critical to individualise care planning for the birth. We aim to explore whether radiomic analysis of T2-weighted magnetic resonance imaging (MRI) can predict severe cases by distinguishing between histopathological subtypes antenatally. Methods This was a bi-centre retrospective analysis of a prospective cohort study conducted between 2018 and 2022. Women who underwent MRI during pregnancy and had histological confirmation of PAS were included. Radiomic features were extracted from T2-weighted images. Univariate regression and multivariate analyses were performed to build predictive models to differentiate between non-invasive (International Federation of Gynecology and Obstetrics [FIGO] grade 1 or 2) and invasive (FIGO grade 3) PAS using R software. Prediction performance was assessed based on several metrics including sensitivity, specificity, accuracy and area under the curve (AUC) at receiver operating characteristic analysis. Results Forty-one women met the inclusion criteria. At univariate analysis, 0.64 sensitivity (95% confidence interval [CI] 0.0−1.00), specificity 0.93 (0.38−1.0), 0.58 accuracy (0.37−0.78) and 0.77 AUC (0.56−.097) was achieved for predicting severe FIGO grade 3 PAS. Using a multivariate approach, a support vector machine model yielded 0.30 sensitivity (95% CI 0.18−1.0]), 0.74 specificity (0.38−1.00), 0.58 accuracy (0.40−0.82), and 0.53 AUC (0.40−0.85). Conclusion Our results demonstrate a predictive potential of this machine learning pipeline for classifying severe PAS cases. Relevance statement This study demonstrates the potential use of radiomics from MR images to identify severe cases of placenta accreta spectrum antenatally. Key points • Identifying severe cases of placenta accreta spectrum from imaging is challenging. • We present a methodological approach for radiomics-based prediction of placenta accreta. • We report certain radiomic features are able to predict severe PAS subtypes. • Identifying severe PAS subtypes ensures safe and individualised care planning for birth. Graphical Abstract

Keywords

• Machine learning
• Magnetic resonance imaging
• Placenta accreta
• Pregnancy
• Radiomics