Raine syndrome: Report of a novel mutation and review of the different antenatal imaging modalities used to diagnose this disease.

INTRODUCTION: Raine syndrome is an autosomal recessive disorder characterized mainly by the presence of exophthalmos, choanal atresia or stenosis, osteosclerosis, and cerebral calcifications. There are around 50 cases described in the literature with a prevalence of less than 1/1,000,000. It is secondary to pathogenic variants in the FAM20 C gene, located on chromosome 7p22.3. CASE REPORT: We report a consanguineous family with three affected pregnancies. In the first two, exophthalmos and bone abnormalities were noted, ending in one intra-uterine demise and one neonatal death, without identifying any genetic disorder. During the couple's most recent pregnancy, fetal anomaly sonogram and fetal CT scan revealed microcephaly, intracranial calcifications, exophthalmos, hypertelorism, depressed nasal bridge, midface hypoplasia and thoracic hypoplasia. Fetal blood sampling for whole exome sequencing revealed a novel pathogenic homozygous variant c.1363+1G > A in the FAM20 C gene associated with Raine syndrome. Delivery occurred at 26 weeks of gestation after rupture of membranes followed by neonatal death due to respiratory failure. REVIEW: A review of the distinctive features of Raine syndrome, the contribution of different prenatal imaging modalities (Ultrasound, Computed Tomography and Magnetic Resonance Imaging) in making the diagnosis and the molecular characterization of this disorder is provided.

Variable prenatal presentation of Pfeiffer syndrome: Suggested aids to prenatal sonographic diagnosis.

BACKGROUND: Our purpose was to describe and compare the cranial and extracranial abnormalities of Pfeiffer syndrome on prenatal imaging with postnatal or postmortem findings, which may help in prenatal diagnosis of Pfeiffer syndrome (PS). METHODS: Cases of fetuses with a confirmed diagnosis of PS over a 4-year period (2012-2016) were retrospectively reviewed. Prenatal imaging findings, postnatal, or postmortem investigations and genetic test results were analyzed. RESULTS: Four fetuses were ascertained, 3 with prenatal sonographic findings compatible with PS and one only diagnosed at postmortem. Cases were referred between 22 and 24 weeks' gestation. Three of the 4 cases were terminated, and details of postmortem/postnatal examination were available in all. There was variable presentation of features. Craniosynostosis was present in 3 cases, but only detected prenatally in 2. Extracranial signs included abnormalities of thumbs and/or big toes, detected prenatally in 3 of the 4 cases. A sacral appendage and vertebral or coronal clefts were present at postmortem in 3 cases but only detected prenatally in one. A cartilaginous tracheal sleeve was detected at postmortem in all 3 cases but not detected by prenatal ultrasound. Other findings included ventriculomegaly, posterior fossa, and facial anomalies. Molecular testing revealed mutations of the fibroblast growth factor receptor 2 (FGFR2) gene in all cases. CONCLUSION: Pfeiffer syndrome has a highly variable phenotype, and the absence of craniosynostosis on prenatal US does not exclude the diagnosis. Presence of abnormal thumbs and big toes, a sacral appendage, vertebral fusions, and coronal clefts should lead to prenatal molecular testing for PS.

Radiomic study of antenatal prediction of severe placenta accreta spectrum from MRI.

OBJECTIVES: We previously demonstrated the potential of radiomics for the prediction of severe histological placenta accreta spectrum (PAS) subtypes using T2-weighted MRI. We aim to validate our model using an additional dataset. Secondly, we explore whether the performance is improved using a new approach to develop a new multivariate radiomics model. METHODS: Multi-centre retrospective analysis was conducted between 2018 and 2023. Inclusion criteria: MRI performed for suspicion of PAS from ultrasound, clinical findings of PAS at laparotomy and/or histopathological confirmation. Radiomic features were extracted from T2-weighted MRI. The previous multivariate model was validated. Secondly, a 5-radiomic feature random forest classifier was selected from a randomized feature selection scheme to predict invasive placenta increta PAS cases. Prediction performance was assessed based on several metrics including area under the curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity, and specificity. RESULTS: We present 100 women [mean age 34.6 (±3.9) with PAS], 64 of whom had placenta increta. Firstly, we validated the previous multivariate model and found that a support vector machine classifier had a sensitivity of 0.620 (95% CI: 0.068; 1.0), specificity of 0.619 (95% CI: 0.059; 1.0), an AUC of 0.671 (95% CI: 0.440; 0.922), and accuracy of 0.602 (95% CI: 0.353; 0.817) for predicting placenta increta. From the new multivariate model, the best 5-feature subset was selected via the random subset feature selection scheme comprised of 4 radiomic features and 1 clinical variable (number of previous caesareans). This clinical-radiomic model achieved an AUC of 0.713 (95% CI: 0.551; 0.854), accuracy of 0.695 (95% CI 0.563; 0.793), sensitivity of 0.843 (95% CI 0.682; 0.990), and specificity of 0.447 (95% CI 0.167; 0.667). CONCLUSION: We validated our previous model and present a new multivariate radiomic model for the prediction of severe placenta increta from a well-defined, cohort of PAS cases. ADVANCES IN KNOWLEDGE: Radiomic features demonstrate good predictive potential for identifying placenta increta. This suggests radiomics may be a useful adjunct to clinicians caring for women with this high-risk pregnancy condition.

Radiomics-based prediction of FIGO grade for placenta accreta spectrum.

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.

Heterogeneous colonic content: A prenatal sonographic manifestation of lysinuric protein intolerance.

We report a fetus with heterogeneous colonic content, an isolated sonographic prenatal sign of lysinuric protein intolerance, a very rare metabolic disease. Familial genetic enquiries confirmed heterozygote mutation in the implicated gene in parents. The prenatal diagnosis led to neonatal dietary adaptation and avoided acute complications.