Normative modeling is increasingly used to characterize typical growth trajectories and identify atypical neurodevelopment, including early brain development using magnetic resonance imaging (MRI) acquired before birth. Recent work has emphasized the importance of large sample sizes for accurate and robust centile estimation. In this study, we investigate how image quality influences fetal brain normative models, a critical factor in this context where MRI is acquired on a moving fetus in utero. Using a multi-centric cohort of 635 fetal MRI scans, we applied a standardized visual quality control (QC) protocol with continuous quality ratings. We fit normative models for multiple brain structures under progressively relaxed QC stringency, and quantified the deviations in centile estimates relative to a high-quality reference subgroup. Our results showed that including lower-quality data systematically biased normative centiles, with the strongest effects observed in the outer centiles, particularly the lower tail (1st-10th). Bias increased progressively as QC stringency was relaxed and could not be attributed solely to the number of scans used to fit the models. Quality-induced bias was structure dependent, and often not visually apparent at the segmentation level. These findings highlight that image quality is an important source of bias in normative fetal brain modeling, and that increasing sample size at the expense of quality may systematically affect centile estimates, potentially jeopardizing the utility of the model.

Fetal brain magnetic resonance imaging (MRI) is crucial for assessing neurodevelopment in utero. However, fetal MRI analysis remains technically challenging due to fetal motion, low signal-to-noise ratio, and the need for complex multi-step processing pipelines. These pipelines typically include motion correction, super-resolution reconstruction, tissue segmentation, and cortical surface extraction. While specialized tools exist for each individual processing step, integrating them into a robust, reproducible, and user-friendly end-to-end workflow remains difficult. This fragmentation limits reproducibility across studies and hinders the adoption of advanced fetal neuroimaging methods in both research and clinical contexts. Fetpype addresses this gap by providing a standardized, modular, and reproducible framework for fetal brain MRI preprocessing and analysis, enabling researchers to process raw T2-weighted acquisitions through to derived volumetric and surface-based outputs within a unified workflow. Fetpype is publicly available on GitHub at https://github.com/fetpype/fetpype.

INTRODUCTION:Severe polyhydramnios is associated with fetal anomalies and adverse perinatal outcomes. The safety of amnioreduction in singleton pregnancies remains unclear. We aimed to evaluate pregnancy outcomes and procedure-related complications of amnioreduction in singleton pregnancies with severe polyhydramnios, excluding other invasive procedures apart from diagnostic amniocentesis. METHODS:Retrospective analysis of amnioreduction procedures performed in 40 singleton pregnancies at a fetal therapy referral center (2013-2024). Inclusion criteria were severe polyhydramnios (defined by an Amniotic Fluid Index ≥35 cm) associated with maternal intolerance and/or short cervix evaluated by ultrasound (with a cut-off <15 mm). Procedure-related complications occurring within 48 h, including premature rupture of membranes, placental abruption, chorioamnionitis, preterm delivery below 37 weeks, and fetal demise were recorded. Maternal and pregnancy data, technical procedural characteristics, and perinatal outcomes were compared between complicated and uncomplicated procedures. RESULTS:In 90% of the cases, a recognizable etiology for polyhydramnios was identified, with fetal structural anomalies being the most frequent cause, accounting for 62.5% of cases. Overall complication rate was 8.8%, including 3 placental abruptions and 2 cases of premature rupture of membranes. Complicated procedures were associated with earlier delivery (31.9 ± 3.7 vs. 36.8 ± 1.9 weeks, p = 0.016) and shorter procedure-to-delivery interval (8.6 ± 14.7 vs. 33.4 ± 16.4 days, p = 0.003). Repeated amnioreductions did not increase complication rates. No significant association was observed between pregnancy- or amniodrainage-related variables - including the number of amnioreduction sessions - and the occurrence of complications. CONCLUSION:These findings support that amnioreduction performed by highly trained personnel following a standardized protocol is a safe intervention in singleton pregnancies complicated by severe polyhydramnios. These data may provide valuable guidance in prenatal counseling for cases in which prolonging gestation is crucial due to fetal structural anomalies.

Objectives: This study aimed to assess the role of olfactory sulci (OS) in diagnosing CHARGE syndrome among fetuses with major congenital heart defects (CHDs). Methods: We prospectively evaluated OS development in fetuses diagnosed with CHDs from 2017 to 2021. Neurosonography (NSG) was performed using transabdominal and transvaginal approaches after 30 weeks of gestation. OS assessment was conducted in the trans-frontal coronal plane, classifying their appearance as fully developed, hypoplastic, or absent. Abnormal OS cases underwent MRI and trio-based clinical exome sequencing (CES). Results: The study included 147 fetuses with CHD. Abnormal OS were found in 4 fetuses (2.7%) which also exhibited other additional anomalies. OS were absent in cases 1-3 and hypoplastic in case 4.. MRI confirmed OS abnormalities in all cases, and trio-based CES identified a CHD7 gene mutation in cases 1, 2, and 4, supporting the diagnosis of CHARGE syndrome. Case 3 had normal trio-based CES results. No other CHARGE syndrome cases were diagnosed postnatally among the cases with normal OS. Conclusions: Systematic evaluation of OS in fetuses with major CHD might contribute to the diagnosis of CHARGE syndrome. Our findings support the inclusion of OS assessment in the prenatal evaluation of fetuses with major CHDs.

Fetal brain MRI is increasingly used to complement ultrasound imaging. Images are processed using complex super-resolution reconstruction pipelines, which may bias biometric and volumetric measurements. To assess the consistency of 2-dimensional (D) biometric and 3-D volumetric measurements across three hospitals using three widely used super-resolution reconstruction pipelines. This retrospective multi-centric study used T2-weighted fetal brain MRI scans acquired at three hospitals between 2009 and 2023. MRIs from each subject were reconstructed with each super-resolution reconstruction method, and biometric measurements were performed by four experts. Automated 3-D volumetry was performed using a state-of-the-art segmentation method. A qualitative evaluation assessed the clinicians' likelihood of using super-resolution reconstructed volumes in their practice. Eighty-four healthy subjects were included. Biometric measurements revealed statistically significant changes that consistently remained below voxel width (0.8 mm; P<0.001). Automated 3-D volumetry revealed small systematic effects (<2.8%; P<0.001). The qualitative evaluation showed systematic differences between super-resolution reconstruction methods for the perception of white matter intensity (P=0.02) and sharpness of the image (P=0.01). Variations in 2-D and 3-D quantitative measurements did not show any large systematic bias when using different super-resolution reconstruction methods for clinical radiological assessment across centers, scanners, and raters.

PURPOSE:The impact of ventriculomegaly (VM) on cortical development and brain functionality has been extensively explored in existing literature. VM has been associated with higher risks of attention-deficit and hyperactivity disorders, as well as cognitive, language, and behavior deficits. Some studies have also shown a relationship between VM and cortical overgrowth, along with reduced cortical folding, both in fetuses and neonates. However, there is a lack of longitudinal studies that study this relationship from fetuses to neonates. METHOD:We used a longitudinal dataset of 30 subjects (15 healthy controls and 15 subjects diagnosed with isolated non-severe VM (INSVM)) with structural MRI acquired in and ex utero for each subject. We focused on the impact of fetal INSVM on cortical development from a longitudinal perspective, from the fetal to the neonatal stage. Particularly, we examined the relationship between ventricular enlargement and both volumetric features and a multifaceted set of cortical folding measures, including local gyrification, sulcal depth, curvature, and cortical thickness. FINDINGS:Our results show significant effects of isolated non-severe VM (INSVM) compared to healthy controls, with reduced cortical thickness in specific brain regions such as the occipital, parietal, and frontal lobes. CONCLUSION:These findings align with existing literature, confirming the presence of alterations in cortical growth and folding in subjects with isolated non-severe VM (INSVM) from the fetal to neonatal stage compared to controls.

Anomalies of the corpus callosum (CC) are amongst the most common fetal Central Nervous System (CNS) anomalies detectable on ultrasound. Underlying genetic disease plays an important part in defining prognosis. Associations with aneuploidy and submicroscopic chromosomal deletions or duplications have been well demonstrated using chromosomal microarray analysis. Next-generation sequencing techniques such as exome sequencing, have revolutionized the ability to detect monogenic disease in these fetuses. In the context of important recent publications on exome sequencing in prenatal populations, an updated review of genetic testing options in CC anomalies is presented.

BACKGROUND:There is a scarcity of evidence of the influence of exposure to air pollution during pregnancy on the human fetal brain characterised prenatally. We aimed to evaluate the association of exposure to air pollution with fetal brain morphology. METHODS:In this prospective cohort study, we used data from the Barcelona Life Study Cohort, Spain, which recruited 1080 pregnant women at 8-14 weeks of gestation between Oct 16, 2018, and April 14, 2021, from three major university hospitals in Barcelona. Eligible participants were aged 18-45 years, had a singleton pregnancy, and had a fetus without major congenital anomalies. Third-trimester transvaginal neurosonography was applied to evaluate fetal brain morphological development. We integrated comprehensive data on time-activity patterns with land use regression, dispersion, and hybrid models to estimate exposure to NO2, PM2·5, and black carbon at home, workplace, and commuting routes during pregnancy until the neurosonography date. Single-pollutant linear mixed regression models and multipollutant ridge regression models were applied to estimate the associations between air pollutants and fetal brain outcomes, controlled for confounders. Distributed lag linear models were used to identify the vulnerable windows. FINDINGS:Among 1080 participants recruited at baseline, 954 attended the follow-up for the neurosonographic examination, 754 of whom were included in this study. In single-pollutant models, we found that prenatal exposure to NO2, PM2·5, and black carbon was associated with a wider anterior horn of lateral ventricles, wider cisterna magna, and larger cerebellar vermis. We also observed that higher exposure to black carbon was related to a shallower Sylvian fissure. No clear pattern or associations were observed between air pollution and other structures of brain morphology. Multipollutant models showed that these associations with black carbon remained significant, whereas associations with PM2·5 and NO2 lost significance for some indicators. A potential vulnerability window in mid-to-late pregnancy was identified for these associations. INTERPRETATION:Exposure to air pollution might affect brain morphological development as early as the fetal stage. Our findings could have important policy implications as they highlight the need to mitigate exposure of pregnant individuals to air pollution in urban areas to protect fetal brain development. FUNDING:European Research Council.

Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where acquisitions and image processing techniques are less standardized than in adult imaging. In this work, we focus on automated quality control of super-resolution reconstruction (SRR) volumes of fetal brain MRI, an important processing step where multiple stacks of thick 2D slices are registered together and combined to build a single, isotropic and artifact-free T2 weighted volume. We propose FetMRQC_SR, a machine-learning method that extracts more than 100 image quality metrics to predict image quality scores using a random forest model. This approach is well suited to a problem that is high dimensional, with highly heterogeneous data and small datasets. We validate FetMRQC_SR in an out-of-domain (OOD) setting and report high performance (ROC AUC = 0.89), even when faced with data from an unknown site or SRR method. We also investigate failure cases and show that they occur in 45% of the images due to ambiguous configurations for which the rating from the expert is arguable. These results are encouraging and illustrate how a non deep learning-based method like FetMRQC_SR is well suited to this multifaceted problem. Our tool, along with all the code used to generate, train and evaluate the model are available at https://github.com/Medical-Image-Analysis-Laboratory/fetmrqc_sr/ .

Anomalies of the corpus callosum (CC) are amongst the most common fetal Central Nervous System (CNS) anomalies detectable on ultrasound. Underlying genetic disease plays an important part in defining prognosis. Associations with aneuploidy and submicroscopic chromosomal deletions or duplications have been well demonstrated using chromosomal microarray analysis. Next-generation sequencing techniques such as exome sequencing, have revolutionized the ability to detect monogenic disease in these fetuses. In the context of important recent publications on exome sequencing in prenatal populations, an updated review of genetic testing options in CC anomalies is presented.

Journal Article Cohort Profile: Barcelona Life Study Cohort (BiSC) Get access Payam Dadvand, Payam Dadvand ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Corresponding author. Barcelona Institute for Global Health (ISGlobal), Parc de Recerca Biomèdica de Barcelona—PRBB, C/Doctor Aiguader, 88, 08003 Barcelona, Spain. E-mail: payam.dadvand@isglobal.org Search for other works by this author on: Oxford Academic PubMed Google Scholar Mireia Gascon, Mireia Gascon ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Mariona Bustamante, Mariona Bustamante ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain https://orcid.org/0000-0003-0127-2860 Search for other works by this author on: Oxford Academic PubMed Google Scholar Ioar Rivas, Ioar Rivas ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Maria Foraster, Maria Foraster ISGlobal, Barcelona, SpainPHAGEX Research Group, Blanquerna School of Health Science, Universitat Ramon Llull (URL), Barcelona, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Xavier Basagaña, Xavier Basagaña ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain https://orcid.org/0000-0002-8457-1489 Search for other works by this author on: Oxford Academic PubMed Google Scholar Marta Cosín, Marta Cosín ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Elisenda Eixarch, Elisenda Eixarch BCNatal, Fetal Medicine Research Center, Hospital Sant Joan de Déu and Hospital Clínic, University of Barcelona, Barcelona, SpainInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, SpainCentre for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Muriel Ferrer, Muriel Ferrer ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Eduard Gratacós, Eduard Gratacós BCNatal, Fetal Medicine Research Center, Hospital Sant Joan de Déu and Hospital Clínic, University of Barcelona, Barcelona, SpainInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, SpainCentre for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar ... Show more Laura Gómez Herrera, Laura Gómez Herrera ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Pol Jimenez-Arenas, Pol Jimenez-Arenas ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Jordi Júlvez, Jordi Júlvez ISGlobal, Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, SpainInstitut d'Investigació Sanitària Pere Virgili (IISPV), Clinical and Epidemiological Neuroscience Group (NeuroÈpia), Reus (Tarragona), Catalonia, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Àlex Morillas, Àlex Morillas ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Mark J Nieuwenhuijsen, Mark J Nieuwenhuijsen ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Cecília Persavento, Cecília Persavento ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Jesús Pujol, Jesús Pujol MRI Research Unit, Department of Radiology, Hospital del Mar, Barcelona, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Xavier Querol, Xavier Querol Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Barcelona, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Olga Sánchez García, Olga Sánchez García Department of Obstetrics and Gynaecology., Institut d'Investigació Biomèdica Sant Pau—IIB Sant Pau. Hospital de la Santa Creu i Sant Pau, Barcelona, SpainPrimary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Origin Network (RICORS), RD21/0012/0001, Instituto de Salud Carlos III, Madrid, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Martine Vrijheid, Martine Vrijheid ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Elisa Llurba, Elisa Llurba Department of Obstetrics and Gynaecology., Institut d'Investigació Biomèdica Sant Pau—IIB Sant Pau. Hospital de la Santa Creu i Sant Pau, Barcelona, SpainPrimary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Origin Network (RICORS), RD21/0012/0001, Instituto de Salud Carlos III, Madrid, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar María Dolores Gómez-Roig, María Dolores Gómez-Roig BCNatal, Fetal Medicine Research Center, Hospital Sant Joan de Déu and Hospital Clínic, University of Barcelona, Barcelona, SpainPrimary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Origin Network (RICORS), RD21/0012/0003, Instituto de Salud Carlos III, Madrid, SpainInstitut de Recerca Sant Joan de Déu, Barcelona, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar Jordi Sunyer, Jordi Sunyer ISGlobal, Barcelona, SpainUniversitat Pompeu Fabra (UPF), Barcelona, SpainCIBER Epidemiología y Salud Pública (CIBERESP), Madrid, SpainIMIM (Hospital del Mar Medical Research Institute), Barcelona, Catalonia, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar BiSC Group BiSC Group Search for other works by this author on: Oxford Academic PubMed Google Scholar International Journal of Epidemiology, Volume 53, Issue 3, June 2024, dyae063, https://doi.org/10.1093/ije/dyae063 Published: 09 May 2024 Article history Received: 09 February 2024 Editorial decision: 27 March 2024 Accepted: 13 April 2024 Published: 09 May 2024

Deformable image registration is a cornerstone of many medical image analysis applications, particularly in the context of fetal brain magnetic resonance imaging (MRI), where precise registration is essential for studying the rapidly evolving fetal brain during pregnancy and potentially identifying neurodevelopmental abnormalities. While deep learning has become the leading approach for medical image registration, traditional convolutional neural networks (CNNs) often fall short in capturing fine image details due to their bias toward low spatial frequencies. To address this challenge, we introduce a deep learning registration framework comprising multiple cascaded convolutional networks. These networks predict a series of incremental deformation fields that transform the moving image at various spatial frequency levels, ensuring accurate alignment with the fixed image. This multi-resolution approach allows for a more accurate and detailed registration process, capturing both coarse and fine image structures. Our method outperforms existing state-of-the-art techniques, including other multi-resolution strategies, by a substantial margin. Furthermore, we integrate our registration method into a multi-atlas segmentation pipeline and showcase its competitive performance compared to nnU-Net, achieved using only a small subset of annotated images as atlases. This approach is particularly valuable in the context of fetal brain MRI, where annotated datasets are limited. Our pipeline for registration and multi-atlas segmentation is publicly available at https://github.com/ValBcn/CasReg.

BACKGROUND:Maternal childhood maltreatment (CM) has been repeatedly associated with negative offspring's emotional outcomes. The dysregulation of the Hypothalamic-Pituitary-Adrenal (HPA) axis has emerged as the main underlying physiological mechanism. OBJECTIVE:To explore the association between maternal CM and newborns' physiological and neurobehavioral stress responses, considering the role of perinatal maternal depression and bonding. PARTICIPANTS AND SETTING:150 healthy women were followed throughout pregnancy. 79 mother-infant dyads were included in the final analyses. Maternal CM was evaluated using the Childhood Trauma Questionnaire and depressive symptoms by the Edinburgh Postnatal Depression Scale (EPDS) at each trimester. At 7 weeks postpartum, the EPDS and the Postpartum Bonding Questionnaire were administered. Newborns' behavioral responses were assessed using "States Organization" (SO) and "States Regulation" (SR) subdomains of the Neonatal Behavioral Assessment Scale (NBAS). Newborns' salivary samples were collected before and after the NBAS to study cortisol reactivity. METHODS:A cross-lagged panel model was employed. RESULTS:Infants born to mothers with higher CM presented more optimal scores on SO (β (0.635) = 0.216, p 〈001) and SR (ß (0.273) = 0.195, p = .006), and a higher cortisol reactivity after NBAS handling (β(0.019) = 0.217, p = .009). Moreover, newborns of mothers with higher CM and postpartum depressive symptoms exhibited a poorer performance on SR (ß (0.156 = -0.288,p = .002). Analyses revealed non-significant relationships between mother-infant bonding, newborns' cortisol reactivity and SO. CONCLUSIONS:Newborns from mothers with greater CM present higher cortisol reactivity and more optimal behavioral responses, which may reflect a prenatal HPA axis sensitization. However, those exposed to maternal postnatal depressive symptoms present poorer stress recovery.

ABSTRACT Objectives To ascertain whether abnormalities in neonatal head circumference and/or body weight are associated with levels of angiogenic/antiangiogenic factors in the maternal and cord blood of pregnancies with a congenital heart defect (CHD) and to assess whether the specific type of CHD influences this association. Methods This was a multicenter case–control study of women carrying a fetus with major CHD. Recruitment was carried out between June 2010 and July 2018 at four tertiary care hospitals in Spain. Maternal venous blood was drawn at study inclusion and at delivery. Cord blood samples were obtained at birth when possible. Placental growth factor (PlGF), soluble fms‐like tyrosine kinase‐1 (sFlt‐1) and soluble endoglin (sEng) were measured in maternal and cord blood. Biomarker concentrations in the maternal blood were expressed as multiples of the median (MoM). Results PlGF, sFlt‐1 and sEng levels were measured in the maternal blood in 237 cases with CHD and 260 healthy controls, and in the cord blood in 150 cases and 56 controls. Compared with controls, median PlGF MoM in maternal blood was significantly lower in the CHD group (0.959 vs 1.022; P < 0.0001), while median sFlt‐1/PlGF ratio MoM was significantly higher (1.032 vs 0.974; P = 0.0085) and no difference was observed in sEng MoM (0.981 vs 1.011; P = 0.4673). Levels of sFlt‐1 and sEng were significantly higher in cord blood obtained from fetuses with CHD compared to controls (mean ± standard error of the mean, 447 ± 51 vs 264 ± 20 pg/mL; P = 0.0470 and 8.30 ± 0.92 vs 5.69 ± 0.34 ng/mL; P = 0.0430, respectively). Concentrations of sFlt‐1 and the sFlt‐1/PlGF ratio in the maternal blood at study inclusion were associated negatively with birth weight and head circumference in the CHD group. The type of CHD anomaly (valvular, conotruncal or left ventricular outflow tract obstruction) did not appear to alter these findings. Conclusions Pregnancies with fetal CHD have an antiangiogenic profile in maternal and cord blood. This imbalance is adversely associated with neonatal head circumference and birth weight. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

ABSTRACT Objective To assess whether the cannula insertion site on the maternal abdomen during fetal endoscopic tracheal occlusion (FETO) for congenital diaphragmatic hernia (CDH) was associated with preterm prelabor rupture of membranes (PPROM) before balloon removal. Methods This was a multicenter retrospective study of consecutive pregnancies with isolated left‐ or right‐sided CDH that underwent FETO in four centers between January 2009 and January 2021. The site for balloon insertion was categorized as above or below the umbilicus. One propensity score was analyzed in both groups to calculate an average treatment effect (ATE) by inverse probability of treatment weighting. Logistic regression and Cox proportional hazard regression including the ATE weights were performed to examine the effect size of entry point on the frequency and timing of PPROM before balloon removal. Results A total of 294 patients were included. The mean ± SD gestational age at PPROM was 33.45 ± 2.01 weeks and the mean rate of PPROM before balloon removal was 25.9% (76/294). Gestational age at FETO was later in the below‐umbilicus group (mean ± SD, 29.47 ± 1.29 weeks vs 29.00 ± 1.25 weeks; P = 0.002) and the duration of FETO was longer in the above‐umbilicus group (median, 14.49 min (interquartile range (IQR), 8.00–21.00 min) vs 11.00 min (IQR, 7.00–14.49 min); P = 0.002). After balancing for possible confounding factors, trocar entry point below the umbilicus did not increase the risk of PPROM before balloon removal (adjusted odds ratio, 1.56 (95% CI, 0.89–2.74); P = 0.120) and had no effect on the timing of PPROM before balloon removal (adjusted hazard ratio, 1.56 (95% CI, 0.95–2.55); P = 0.080). Conclusion There was no evidence that uterine entry site for FETO was correlated with the risk of PPROM before balloon removal. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Fetal brain MRI is becoming an increasingly relevant complement to neurosonography for perinatal diagnosis, allowing fundamental insights into fetal brain development throughout gestation. However, uncontrolled fetal motion and heterogeneity in acquisition protocols lead to data of variable quality, potentially biasing the outcome of subsequent studies. We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data. FetMRQC extracts an ensemble of quality metrics from unprocessed anatomical MRI and combines them to predict experts' ratings using random forests. We validate our framework on a pioneeringly large and diverse dataset of more than 1600 manually rated fetal brain T2-weighted images from four clinical centers and 13 different scanners. Our study shows that FetMRQC's predictions generalize well to unseen data while being interpretable. FetMRQC is a step towards more robust fetal brain neuroimaging, which has the potential to shed new insights on the developing human brain.