ArticleEuropean radiology experimental2023
Radiomics-based prediction of FIGO grade for placenta accreta spectrum.
Article in European radiology experimental, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- Review
- Applicability of an ultrasound scoring system for predicting hemorrhage during cesarean section in patients with placenta accreta spectrum disorders.Frontiers in medicine · 2026Article
- Machine learning applications in placenta accreta spectrum disorders.European journal of obstetrics & gynecology and reproductive biology: X · 2025Review
- Advances in Prenatal Diagnosis of Placenta Accreta Spectrum.Medicina (Kaunas, Lithuania) · 2025Review
- Radiomic study of antenatal prediction of severe placenta accreta spectrum from MRI.The British journal of radiology · 2024Article
- Correction: Radiomics-based prediction of FIGO grade for placenta accreta spectrum.European radiology experimental · 2023Article
- A Review on the Use of Imaging Biomarkers in Oncology Clinical Trials: Quality Assurance Strategies for Technical Validation.Tomography (Ann Arbor, Mich.) · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPlacenta 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.
methodsThis 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.
resultsForty-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).
conclusionOur 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.
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