In vivo placental MRI shape and textural features predict fetal growth restriction and postnatal outcome

Maternal affective symptoms and sleep quality have sex-specific associations with placental topography

BACKGROUND

The impacts of prenatal maternal affective symptoms on the placental structure are not well-established. Employing Geographic Information System (GIS) spatial autocorrelation, Moran's I, can help characterize placental thickness uniformity/variability and evaluate the impacts of maternal distress on placental topography.

METHODS

This study (N = 126) utilized cohort data on prenatal maternal affective symptoms and placental 2D and 3D morphology. Prenatal maternal depression, stress, anxiety and sleep quality were scored for each trimester using the Edinburgh Postnatal Depression Scale (EPDS), Stressful Life Event Scale (SLE), Penn State Worry Questionnaire (PSWQ), and Pittsburgh Sleep Quality Index (PSQI), respectively. Placental shape was divided into Voronoi cells and thickness variability among these cells was computed using Moran's I for 4-nearest neighbors and neighbors within a 10 cm radius. Sex-stratified Spearman correlations and linear regression were used to study associations between mean placental thickness, placental GIS variables, placental weight and the average score of each maternal variable.

RESULTS

For mothers carrying boys, poor sleep was associated with higher mean thickness (r = 0.308,p = 0.035) and lower placental thickness uniformity (r = -0.36,p = 0.012). Lower placental weight (r = 0.395,p = 0.003), higher maternal depression (r = -0.318,p = 0.019) and worry/anxiety (r = -0.362,p = 0.007) were associated with lower placental thickness uniformity for mothers carrying girls.

LIMITATIONS

The study is exploratory and not all GIS models were developed. Excluding high-risk pregnancies prevented investigating pregnancy complications related hypotheses. A larger sample size is needed for greater confidence for clinical application.

CONCLUSIONS

Placental topography can be studied using GIS theory and has shown that prenatal maternal affective symptoms and sleep have sex-specific associations with placental thickness.

A Hybrid Model for Fetal Growth Restriction Assessment by Automatic Placental Radiomics on T2-Weighted MRI and Multifeature Fusion

BACKGROUND

MRI-based placental analyses have been used to improve fetal growth restriction (FGR) assessment by complementing ultrasound-based measurements. However, these are still limited by time-consuming manual annotation in MRI data and the lack of mother-based information.

PURPOSE

To develop and validate a hybrid model for accurate FGR assessment by automatic placental radiomics on T2-weighted imaging (T2WI) and multifeature fusion.

STUDY TYPE

Retrospective.

POPULATION

274 pregnant women (29.5 ± $$ \pm $$  4.0 years) from two centers were included and randomly divided into training (N = 119), internal test (N = 40), time-independent validation (N = 43), and external validation (N = 72) sets.

FIELD STRENGTH/SEQUENCE

1.5-T, T2WI half-Fourier acquisition single-shot turbo spin-echo pulse sequence.

ASSESSMENT

First, the placentas on T2WI were manually annotated, and a deep learning model was developed to automatically segment the placentas. Then, the radiomic features were extracted from the placentas and selected by three-step feature selection. In addition, fetus-based measurement features and mother-based clinical features were obtained from ultrasound examinations and medical records, respectively. Finally, a hybrid model based on random forest was constructed by fusing these features, and further compared with models based on other machine learning methods and different feature combinations.

STATISTICAL TESTS

The performances of placenta segmentation and FGR assessment were evaluated by Dice similarity coefficient (DSC) and the area under the receiver operating characteristic curve (AUROC), respectively. A P-value <0.05 was considered statistically significant.

RESULTS

The placentas were automatically segmented with an average DSC of 90.0%. The hybrid model achieved an AUROC of 0.923, 0.931, and 0.880 on the internal test, time-independent validation, and external validation sets, respectively. The mother-based clinical features resulted in significant performance improvements for FGR assessment.

DATA CONCLUSION

The proposed hybrid model may be able to assess FGR with high accuracy. Furthermore, information complementation based on placental, fetal, and maternal features could also lead to better FGR assessment performance.

LEVEL OF EVIDENCE: 3

TECHNICAL EFFICACY

Stage 2.

Fetal MRI-Based Body and Adiposity Quantification for Small for Gestational Age Perinatal Risk Stratification

BACKGROUND

Small for gestational age (SGA) fetuses are at risk for perinatal adverse outcomes. Fetal body composition reflects the fetal nutrition status and hold promise as potential prognostic indicator. MRI quantification of fetal anthropometrics may enhance SGA risk stratification.

HYPOTHESIS

Smaller, leaner fetuses are malnourished and will experience unfavorable outcomes.

STUDY TYPE

Prospective.

POPULATION

40 SGA fetuses, 26 (61.9%) females: 10/40 (25%) had obstetric interventions due to non-reassuring fetal status (NRFS), and 17/40 (42.5%) experienced adverse neonatal events (CANO). Participants underwent MRI between gestational ages 30 + 2 and 37 + 2.

FIELD STRENGTH/SEQUENCE

3-T, True Fast Imaging with Steady State Free Precession (TruFISP) and T1 -weighted two-point Dixon (T1 W Dixon) sequences.

ASSESSMENT

Total body volume (TBV), fat signal fraction (FSF), and the fat-to-body volumes ratio (FBVR) were extracted from TruFISP and T1 W Dixon images, and computed from automatic fetal body and subcutaneous fat segmentations by deep learning. Subjects were followed until hospital discharge, and obstetric interventions and neonatal adverse events were recorded.

STATISTICAL TESTS

Univariate and multivariate logistic regressions for the association between TBV, FBVR, and FSF and interventions for NRFS and CANO. Fisher's exact test was used to measure the association between sonographic FGR criteria and perinatal outcomes. Sensitivity, specificity, positive and negative predictive values, and accuracy were calculated. A P-value <0.05 was considered statistically significant.

RESULTS

FBVR (odds ratio [OR] 0.39, 95% confidence interval [CI] 0.2-0.76) and FSF (OR 0.95, CI 0.91-0.99) were linked with NRFS interventions. Furthermore, TBV (OR 0.69, CI 0.56-0.86) and FSF (OR 0.96, CI 0.93-0.99) were linked to CANO. The FBVR sensitivity/specificity for obstetric interventions was 85.7%/87.5%, and the TBV sensitivity/specificity for CANO was 82.35%/86.4%. The sonographic criteria sensitivity/specificity for obstetric interventions was 100%/33.3% and insignificant for CANO (P = 0.145).

DATA CONCLUSION

Reduced TBV and FBVR may be associated with higher rates of obstetric interventions for NRFS and CANO.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 5.

Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging

Central nervous system abnormalities in fetuses are fairly common, happening in 0.1% to 0.2% of live births and in 3% to 6% of stillbirths. So initial detection and categorization of fetal Brain abnormalities are critical. Manually detecting and segmenting fetal brain magnetic resonance imaging (MRI) could be time-consuming, and susceptible to interpreter experience. Artificial intelligence (AI) algorithms and machine learning approaches have a high potential for assisting in the early detection of these problems, improving the diagnosis process and follow-up procedures. The use of AI and machine learning techniques in fetal brain MRI was the subject of this narrative review paper. Using AI, anatomic fetal brain MRI processing has investigated models to predict specific landmarks and segmentation automatically. All gestation age weeks (17-38 wk) and different AI models (mainly Convolutional Neural Network and U-Net) have been used. Some models' accuracy achieved 95% and more. AI could help preprocess and post-process fetal images and reconstruct images. Also, AI can be used for gestational age prediction (with one-week accuracy), fetal brain extraction, fetal brain segmentation, and placenta detection. Some fetal brain linear measurements, such as Cerebral and Bone Biparietal Diameter, have been suggested. Classification of brain pathology was studied using diagonal quadratic discriminates analysis, K-nearest neighbor, random forest, naive Bayes, and radial basis function neural network classifiers. Deep learning methods will become more powerful as more large-scale, labeled datasets become available. Having shared fetal brain MRI datasets is crucial because there aren not many fetal brain pictures available. Also, physicians should be aware of AI's function in fetal brain MRI, particularly neuroradiologists, general radiologists, and perinatologists.

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

BACKGROUND

IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR.

METHODS

We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures.

RESULTS

We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80-0.88), specificity = 0.87 (95% CI 0.83-0.90), positive predictive value = 0.78 (95% CI 0.68-0.86), negative predictive value = 0.91 (95% CI 0.86-0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34-49.59). In detail, the RF-SVM (Random Forest-Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG.

CONCLUSIONS

our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized.

Influence of maternal psychological distress during COVID-19 pandemic on placental morphometry and texture

The Coronavirus Disease 2019 (COVID-19) pandemic has been accompanied by increased prenatal maternal distress (PMD). PMD is associated with adverse pregnancy outcomes which may be mediated by the placenta. However, the potential impact of the pandemic on in vivo placental development remains unknown. To examine the impact of the pandemic and PMD on in vivo structural placental development using advanced magnetic resonance imaging (MRI), acquired anatomic images of the placenta from 63 pregnant women without known COVID-19 exposure during the pandemic and 165 pre-pandemic controls. Measures of placental morphometry and texture were extracted. PMD was determined from validated questionnaires. Generalized estimating equations were utilized to compare differences in PMD placental features between COVID-era and pre-pandemic cohorts. Maternal stress and depression scores were significantly higher in the pandemic cohort. Placental volume, thickness, gray level kurtosis, skewness and run length non-uniformity were increased in the pandemic cohort, while placental elongation, mean gray level and long run emphasis were decreased. PMD was a mediator of the association between pandemic status and placental features. Altered in vivo placental structure during the pandemic suggests an underappreciated link between disturbances in maternal environment and perturbed placental development. The long-term impact on offspring is currently under investigation.

Evaluation of Spatial Attentive Deep Learning for Automatic Placental Segmentation on Longitudinal MRI

BACKGROUND

Automated segmentation of the placenta by MRI in early pregnancy may help predict normal and aberrant placenta function, which could improve the efficiency of placental assessment and the prediction of pregnancy outcomes. An automated segmentation method that works at one gestational age may not transfer effectively to other gestational ages.

PURPOSE

To evaluate a spatial attentive deep learning method (SADL) for automated placental segmentation on longitudinal placental MRI scans.

STUDY TYPE

Prospective, single-center.

SUBJECTS

A total of 154 pregnant women who underwent MRI scans at both 14-18 weeks of gestation and at 19-24 weeks of gestation, divided into training (N = 108), validation (N = 15), and independent testing datasets (N = 31).

FIELD STRENGTH/SEQUENCE

A 3 T, T2-weighted half Fourier single-shot turbo spin-echo (T2-HASTE) sequence.

ASSESSMENT

The reference standard of placental segmentation was manual delineation on T2-HASTE by a third-year neonatology clinical fellow (B.L.) under the supervision of an experienced maternal-fetal medicine specialist (C.J. with 20 years of experience) and an MRI scientist (K.S. with 19 years of experience).

STATISTICAL TESTS

The three-dimensional Dice similarity coefficient (DSC) was used to measure the automated segmentation performance compared to the manual placental segmentation. A paired t-test was used to compare the DSCs between SADL and U-Net methods. A Bland-Altman plot was used to analyze the agreement between manual and automated placental volume measurements. A P value < 0.05 was considered statistically significant.

RESULTS

In the testing dataset, SADL achieved average DSCs of 0.83 ± 0.06 and 0.84 ± 0.05 in the first and second MRI, which were significantly higher than those achieved by U-Net (0.77 ± 0.08 and 0.76 ± 0.10, respectively). A total of 6 out of 62 MRI scans (9.6%) had volume measurement differences between the SADL-based automated and manual volume measurements that were out of 95% limits of agreement.

DATA CONCLUSIONS

SADL can automatically detect and segment the placenta with high performance in MRI at two different gestational ages.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 2.

Predicting the risk of fetal growth restriction by radiomics analysis of the placenta on T2WI: A retrospective case-control study

INTRODUCTION

Fetal growth restriction (FGR) is associated with placental abnormalities, and its precise diagnosis is challenging. This study aimed to explore the role of radiomics based on placental MRI in predicting FGR.

METHODS

A retrospective study using T2-weighted placental MRI data were conducted. A total of 960 radiomic features were automatically extracted. Features were selected using three-step machine learning methods. A combined model was constructed by combining MRI-based radiomic features and ultrasound-based fetal measurements. The receiver operating characteristic curves (ROC) were conducted to assess model performance. Additionally, decision curves and calibration curves were performed to evaluate prediction consistency of different models.

RESULTS

Among the study participants, pregnant women who delivered from January 2015 to June 2021 were randomly divided into training (n = 119) and test (n = 40) sets. Forty-three other pregnant women who delivered from July 2021 to December 2021 were used as the time-independent validation set. After training and testing, three radiomic features that were strongly correlated with FGR were selected. The area under the ROC curves (AUCs) of the MRI-based radiomics model reached 0.87 (95% confidence interval [CI]: 0.74-0.96) and 0.87 (95% CI: 0.76-0.97) in the test and validation sets, respectively. Moreover, the AUCs for the model comprising MRI-based radiomic features and ultrasound-based measurements were 0.91 (95% CI: 0.83-0.97) and 0.94 (95% CI: 0.86-0.99) in the test and validation sets, respectively.

DISCUSSION

MRI-based placental radiomics could accurately predict FGR. Moreover, combining placental MRI-based radiomic features with ultrasound indicators of the fetus could improve the diagnostic accuracy of FGR.

Fetal growth restriction and stillbirth: Biomarkers for identifying at risk fetuses

Fetal growth restriction (FGR) is a major cause of stillbirth, prematurity and impaired neurodevelopment. Its etiology is multifactorial, but many cases are related to impaired placental development and dysfunction, with reduced nutrient and oxygen supply. The fetus has a remarkable ability to respond to hypoxic challenges and mounts protective adaptations to match growth to reduced nutrient availability. However, with progressive placental dysfunction, chronic hypoxia may progress to a level where fetus can no longer adapt, or there may be superimposed acute hypoxic events. Improving detection and effective monitoring of progression is critical for the management of complicated pregnancies to balance the risk of worsening fetal oxygen deprivation in utero, against the consequences of iatrogenic preterm birth. Current surveillance modalities include frequent fetal Doppler ultrasound, and fetal heart rate monitoring. However, nearly half of FGR cases are not detected in utero, and conventional surveillance does not prevent a high proportion of stillbirths. We review diagnostic challenges and limitations in current screening and monitoring practices and discuss potential ways to better identify FGR, and, critically, to identify the “tipping point” when a chronically hypoxic fetus is at risk of progressive acidosis and stillbirth.

Measuring intrauterine growth in healthy pregnancies using quantitative magnetic resonance imaging

OBJECTIVE

The aim of this study was to determine in utero fetal-placental growth patterns using in vivo three-dimensional (3D) quantitative magnetic resonance imaging (qMRI).

STUDY DESIGN

Healthy women with singleton pregnancies underwent fetal MRI to measure fetal body, placenta, and amniotic space volumes. The fetal-placental ratio (FPR) was derived using 3D fetal body and placental volumes (PV). Descriptive statistics were used to describe the association of each measurement with increasing gestational age (GA) at MRI.

RESULTS

Fifty-eight (58) women underwent fetal MRI between 16 and 38 completed weeks gestation (mean = 28.12 ± 6.33). PV and FPR varied linearly with GA at MRI (r_PV,GA = 0.83, r_FPR,GA = 0.89, p value < 0.001). Fetal volume varied non-linearly with GA (p value < 0.01).

CONCLUSIONS

We describe in-utero growth trajectories of fetal-placental volumes in healthy pregnancies using qMRI. Understanding healthy in utero development can establish normative benchmarks where departures from normal may identify early in utero placental failure prior to the onset of fetal harm.

A computerized diagnostic model for automatically evaluating placenta accrete spectrum disorders based on the combined MR radiomics-clinical signatures

We aimed to establish a computerized diagnostic model to predict placenta accrete spectrum (PAS) disorders based on T2-weighted MR imaging. We recruited pregnant women with clinically suspected PAS disorders between January 2015 and December 2018 in our institution. All preoperative T2-weighted imaging (T2WI) MR images were manually outlined on the picture archive communication system terminal server. A nnU-Net network for automatic segmentation and the corresponding radiomics features extracted from the segmented region were applied to build a radiomics-clinical model for PAS disorders identification. Taking the surgical or pathological findings as the reference standard, we compared this computerized model’s diagnostic performance in detecting PAS disorders. In the training cohort, our model combining both radiomics and clinical characteristics yielded an accuracy of 0.771, a sensitivity of 0.854, and a specificity of 0.750 in identifying PAS disorders. In the testing cohort, this model achieved a segmentation mean Dice coefficient of 0.890 and yielded an accuracy of 0.825, a sensitivity of 0.830 and a specificity of 0.822. In the external validation cohort, this computer-aided diagnostic model yielded an accuracy of 0.690, a sensitivity of 0.929 and a specificity of 0.467 in identifying placenta increta. In the present study, a machine learning model based on preoperative T2WI-based imaging had high accuracy in identifying PAS disorders in respect of surgical and histological findings.

Adverse Prenatal Exposures and Fetal Brain Development: Insights From Advanced Fetal Magnetic Resonance Imaging

Converging evidence from clinical and preclinical studies suggests that fetal vulnerability to adverse prenatal exposures increases the risk for neuropsychiatric diseases such as autism spectrum disorder, schizophrenia, and depression. Recent advances in fetal magnetic resonance imaging have allowed us to characterize typical fetal brain growth trajectories in vivo and to interrogate structural and functional alterations associated with intrauterine exposures, such as maternal stress, environmental toxins, drugs, and obesity. Here, we review proposed mechanisms for how prenatal influences disrupt neurodevelopment, including the role played by maternal and fetal inflammatory responses. We summarize insights from magnetic resonance imaging research in fetuses, highlight recent discoveries in normative fetal development using quantitative magnetic resonance imaging techniques (i.e., three-dimensional volumetry, proton magnetic resonance spectroscopy, placental diffusion imaging, and functional imaging), and discuss how baseline trajectories are shaped by prenatal exposures.

Artificial intelligence applied to fetal MRI: A scoping review of current research

Artificial intelligence (AI) is defined as the development of computer systems to perform tasks normally requiring human intelligence. A subset of AI, known as machine learning (ML), takes this further by drawing inferences from patterns in data to 'learn' and 'adapt' without explicit instructions meaning that computer systems can 'evolve' and hopefully improve without necessarily requiring external human influences. The potential for this novel technology has resulted in great interest from the medical community regarding how it can be applied in healthcare. Within radiology, the focus has mostly been for applications in oncological imaging, although new roles in other subspecialty fields are slowly emerging.In this scoping review, we performed a literature search of the current state-of-the-art and emerging trends for the use of artificial intelligence as applied to fetal magnetic resonance imaging (MRI). Our search yielded several publications covering AI tools for anatomical organ segmentation, improved imaging sequences and aiding in diagnostic applications such as automated biometric fetal measurements and the detection of congenital and acquired abnormalities. We highlight our own perceived gaps in this literature and suggest future avenues for further research. It is our hope that the information presented highlights the varied ways and potential that novel digital technology could make an impact to future clinical practice with regards to fetal MRI.

Deep learning-based segmentation of the placenta and uterus on MR images

Purpose: Magnetic resonance imaging has been recently used to examine the abnormalities of the placenta during pregnancy. Segmentation of the placenta and uterine cavity allows quantitative measures and further analyses of the organs. The objective of this study is to develop a segmentation method with minimal user interaction. Approach: We developed a fully convolutional neural network (CNN) for simultaneous segmentation of the uterine cavity and placenta in three dimensions (3D) while a minimal operator interaction was incorporated for training and testing of the network. The user interaction guided the network to localize the placenta more accurately. In the experiments, we trained two CNNs, one using 70 normal training cases and the other using 129 training cases including normal cases as well as cases with suspected placenta accreta spectrum (PAS). We evaluated the performance of the segmentation algorithms on two test sets: one with 20 normal cases and the other with 50 images from both normal women and women with suspected PAS. Results: For the normal test data, the average Dice similarity coefficient (DSC) was 92% and 82% for the uterine cavity and placenta, respectively. For the combination of normal and abnormal cases, the DSC was 88% and 83% for the uterine cavity and placenta, respectively. The 3D segmentation algorithm estimated the volume of the normal and abnormal uterine cavity and placenta with average volume estimation errors of 4% and 9%, respectively. Conclusions: The deep learning-based segmentation method provides a useful tool for volume estimation and analysis of the placenta and uterus cavity in human placental imaging.

Emerging placental biomarkers of health and disease through advanced magnetic resonance imaging (MRI)

Placental dysfunction is a major cause of fetal demise, fetal growth restriction, and preterm birth, as well as significant maternal morbidity and mortality. Infant survivors of placental dysfunction are at elevatedrisk for lifelong neuropsychiatric morbidity. However, despite the significant consequences of placental disease, there are no clinical tools to directly and non-invasively assess and measure placental function in pregnancy. In this work, we will review advanced MRI techniques applied to the study of the in vivo human placenta in order to better detail placental structure, architecture, and function. We will discuss the potential of these measures to serve as optimal biomarkers of placental dysfunction and review the evidence of these tools in the discrimination of health and disease in pregnancy. Efforts to advance our understanding of in vivo placental development are necessary if we are to optimize healthy pregnancy outcomes and prevent brain injury in successive generations. Current management of many high-risk pregnancies cannot address placental maldevelopment or injury, given the standard tools available to clinicians. Once accurate biomarkers of placental development and function are constructed, the subsequent steps will be to introduce maternal and fetal therapeutics targeting at optimizing placental function. Applying these biomarkers in future studies will allow for real-time assessments of safety and efficacy of novel interventions aimed at improving maternal-fetal well-being.

Morphological and histopathological changes in placentas of pregnancies with intrauterine growth restriction

Aim: The definition of fetal growth restriction (FGR) refers to the incapability of a fetus to achieve the appropriate estimated growth, with expected fetal weight below the 10^th percentile calculated for its gestational age. Placental factors and hypoxemia are considered to be essential elements with influence on intrauterine growth restriction (IUGR) and fetal death. The purpose of the present study was to investigate the macroscopic and microscopic pathological findings regarding the placentas in pregnancies complicated by influence on IUGR. Patients, Materials and Methods: Our study included 42 third-trimester pregnant patients admitted to the Cuza Vodă Hospital of Obstetrics and Gynecology, Iaşi, Romania, in the last three years. Soon after delivery, the 42 placentas were collected and analyzed; 32 placentas came from cases previously diagnosed with influence on IUGR and were included in our study group. Ten other placentas included in the control group were selected from uncomplicated pregnancies. Standard Hematoxylin–Eosin (HE) staining method, as well as Periodic Acid–Schiff (PAS) staining, and immunohistochemical techniques for cluster of differentiation 31 (CD31) and collagen IV were used in order to highlight the morphological features of the studied placentas. Results: Our study revealed that reduced placental dimensions and eccentric umbilical cord insertion are correlated with the birthweight of the fetuses with IUGR (p<0.05). The most common histological finding in our study group was placental infarction later correlated with IUGR, but a certain causality could not be demonstrated, as this finding was also present in normal pregnancies. Other histopathological findings were also present in the influence on IUGR group, such as fibrin deposits, diffuse calcification, chronic villitis, avascular chronical villi, with no significant statistical correlations. CD31 was strongly immunoexpressed in the villous endothelial cells. Collagen IV presented a strong immunoreaction in the basement membrane and mesenchyme of the placental villi. Conclusions: Our study revealed a correlation between the dimensions of the diameters and volume of the maternal placenta and the presence of influence on IUGR. Moreover, it confirms the available data suggesting that the place of insertion of the umbilical cord is correlated with the weight of the fetus. Further studies with extended panel antibodies are needed in order to determine and complete the role of these morphological changes in the development of influence on IUGR.

Normative placental structure in pregnancy using quantitative Magnetic Resonance Imaging

INTRODUCTION

To characterize normative morphometric, textural and microstructural placental development by applying advanced and quantitative magnetic resonance imaging (qMRI) techniques to the in-vivo placenta.

METHODS

We enrolled 195 women with uncomplicated, healthy singleton pregnancies in a prospective observational study. Women underwent MRI between 16- and 40-weeks' gestation. Morphometric and textural metrics of placental growth were calculated from T2-weighted (T2W) images, while measures of microstructural development were calculated from diffusion-weighted images (DWI). Normative tables and reference curves were constructed for each measured index across gestation and according to fetal sex.

RESULTS

Data from 269 MRI studies from 169 pregnant women were included in the analyses. During the study period, placentas undergo significant increases in morphometric measures of volume, thickness, and elongation. Placental texture reveals increasing variability with advancing gestation as measured by grey level non uniformity, run length non uniformity and long run high grey level emphasis. Placental microstructure did not vary with gestational age. Placental elongation was the only metric that differed significantly between male and female fetuses.

DISCUSSION

We report quantitative metrics of placental morphometry, texture and microstructure in a large cohort of healthy controls during the second and third trimesters of pregnancy. These measures can serve as normative references of in-vivo placental development to better understand placental function in high-risk conditions and allow for the early detection of placental mal-development.

The placenta in fetal growth restriction: What is going wrong?

The placenta is essential for the efficient delivery of nutrients and oxygen from mother to fetus to maintain normal fetal growth. Dysfunctional placental development underpins many pregnancy complications, including fetal growth restriction (FGR) a condition in which the fetus does not reach its growth potential. The FGR placenta is smaller than normal placentae throughout gestation and displays maldevelopment of both the placental villi and the fetal vasculature within these villi. Specialized epithelial cells called trophoblasts exhibit abnormal function and development in FGR placentae. This includes an altered balance between proliferation and apoptotic death, premature cellular senescence, and reduced colonisation of the maternal decidual tissue. Thus, the placenta undergoes aberrant changes at the macroscopic to cellular level in FGR, which can limit exchange capacity and downstream fetal growth. This review aims to compile stereological, in vitro, and imaging data to create a holistic overview of the FGR placenta and its pathophysiology, with a focus on the contribution of trophoblasts.

Segmentation of uterus and placenta in MR images using a fully convolutional neural network

Segmentation of the uterine cavity and placenta in fetal magnetic resonance (MR) imaging is useful for the detection of abnormalities that affect maternal and fetal health. In this study, we used a fully convolutional neural network for 3D segmentation of the uterine cavity and placenta while a minimal operator interaction was incorporated for training and testing the network. The user interaction guided the network to localize the placenta more accurately. We trained the network with 70 training and 10 validation MRI cases and evaluated the algorithm segmentation performance using 20 cases. The average Dice similarity coefficient was 92% and 82% for the uterine cavity and placenta, respectively. The algorithm could estimate the volume of the uterine cavity and placenta with average errors of 2% and 9%, respectively. The results demonstrate that the deep learning-based segmentation and volume estimation is possible and can potentially be useful for clinical applications of human placental imaging.

Placental MRI and its application to fetal intervention

OBJECTIVE

Magnetic resonance imaging (MRI) of placental invasion has been part of clinical practice for many years. The possibility of being better able to assess placental vascularization and function using MRI has multiple potential applications. This review summarises up-to-date research on placental function using different MRI modalities.

METHOD

We discuss how combinations of these MRI techniques have much to contribute to fetal conditions amenable for therapy such as singletons at high risk for fetal growth restriction (FGR) and monochorionic twin pregnancies for planning surgery and counselling for selective growth restriction and transfusion conditions.

RESULTS

The whole placenta can easily be visualized on MRI, with a clear boundary against the amniotic fluid, and a less clear placental-uterine boundary. Contrasts such as diffusion weighted imaging, relaxometry, blood oxygenation level dependent MRI and flow and metabolite measurement by dynamic contrast enhanced MRI, arterial spin labeling, or spectroscopic techniques are contributing to our wider understanding of placental function.

CONCLUSION

The future of placental MRI is exciting, with the increasing availability of multiple contrasts and new models that will boost the capability of MRI to measure oxygen saturation and placental exchange, enabling examination of placental function in complicated pregnancies.

T2* relaxometry to characterize normal placental development over gestation in-vivo at 3T

Background: T2* relaxometry has been identified as a non-invasive way to study the placenta in-vivo with good potential to identify placental insufficiency. Typical interpretation links T2* values to oxygen concentrations. This study aimed to comprehensively assess T2* maps as a marker of placental oxygenation in-vivo. Methods: A multi-echo gradient echo echo planar imaging sequence is used in a cohort of 84 healthy pregnant women. Special emphasis is put on spatial analysis: histogram measures, Histogram Asymmetry Measure (HAM) and lacunarity. Influences of maternal, fetal and placental factors and experimental parameters on the proposed measures are evaluated. Results: T2* maps were obtained from each placenta in less than 30sec. The previously reported decreasing trend in mean T2* with gestation was confirmed (3.45 ms decline per week). Factors such as maternal age, BMI, fetal sex, parity, mode of delivery and placental location were shown to be uncorrelated with T2* once corrected for gestational age. Robustness of the obtained values with regard to variation in segmentation and voxel-size were established. The proposed spatially resolved measures reveal a change in T2* in late gestation. Conclusions: T2* mapping is a robust and quick technique allowing quantification of both whole volume and spatial quantification largely independent of confounding factors.

The placental transcriptome of the first-trimester placenta is affected by in vitro fertilization and embryo transfer

BACKGROUND

The placenta is a highly specialized temporary organ that is related to fetal development and pregnancy outcomes, and epidemiological data demonstrate an increased risk of placental abnormality after in vitro fertilization and embryo transfer (IVF-ET).

METHODS

This study examines alterations in the transcriptome profile of first-trimester placentas from IVF-ET pregnancies and analyzes the potential mechanisms that play a role in the adverse perinatal outcomes associated with IVF-ET procedures. Four human placental villi from first-trimester samples were obtained through fetal bud aspiration from patients subjected to IVF-ET due to oviductal factors. An additional four control human placental villi were derived from a group of subjects who spontaneously conceived a twin pregnancy. We analyzed their transcriptomes by microarray. Then, RT-qPCR and immunohistochemistry were utilized to analyze several dysregulated genes to validate the microarray results. Biological functions and pathways were analyzed with bioinformatics tools.

RESULTS

A total of 3405 differentially regulated genes were identified as significantly dysregulated (> 2-fold change; P < 0.05) in the IVF-ET placenta in the first trimester: 1910 upregulated and 1495 downregulated genes. Functional enrichment analysis of the differentially regulated genes demonstrated that the genes were involved in more than 50 biological processes and pathways that have been shown to play important roles in the first trimester in vivo. These pathways can be clustered into coagulation cascades, immune response, transmembrane signaling, metabolism, cell cycle, stress control, invasion and vascularization. Nearly the same number of up- and downregulated genes participate in the same biological processes related to placental development and maintenance. Procedures utilized in IVF-ET altered the expression of first-trimester placental genes that are critical to these biological processes and triggered a compensatory mechanism during early implantation in vivo.

CONCLUSION

These data provide a potential basis for further analysis of the higher frequency of adverse perinatal outcomes following IVF-ET, with the ultimate goal of developing safer IVF-ET protocols.

Apparent Diffusion Coefficient of the Placenta and Fetal Organs in Intrauterine Growth Restriction

PURPOSE

This study aimed to assess apparent diffusion coefficient (ADC) of the placenta and fetal organs in intrauterine growth restriction (IUGR).

MATERIALS AND METHODS

A prospective study of 30 consecutive pregnant women (aged 21-38 years with mean age of 31.5 years and a mean gestational week of 35 ± 2.3) with IUGR and 15 age-matched pregnant women was conducted. All patients and controls underwent diffusion-weighted magnetic resonance imaging. The ADCs of the placenta and fetal brain, kidney, and lung were calculated and correlated with neonates needing intensive care unit (ICU) admission.

RESULTS

There was a significant difference in ADC of the placenta and fetal brain, lung, and kidney (P = 0.001, 0.001, 0.04, and 0.04, respectively) between the patients and the controls. The cutoff ADCs of the placenta and fetal brain, lung, and kidney used to detect IUGR were 1.45, 1.15, 1.80, and 1.40 × 10 mm/s, respectively, with areas under the curve (AUCs) of 0.865, 0.858, 0.812, and 0.650, respectively, and accuracy values of 75%, 72.5%, 72.5%, and 70%, respectively. Combined ADC of the placenta and fetal organs used to detect IUGR revealed an AUC of 1.00 and an accuracy of 100%. There was a significant difference in ADC of the placenta and fetal brain, lung, and kidney between neonates needing admission and those not needing ICU admission (P = 0.001, 0.001, 0.002, and 0.002, respectively). The cutoff ADCs of the placenta and fetal brain, lung, and kidney used to define neonates needing ICU were 1.35, 1.25, 1.95, and 1.15 × 10 mm/s with AUCs of 0.955, 0.880, 0.884, and 0.793, respectively, and accuracy values of 86.7%, 46.7%, 76.7%, and 70%, respectively. Combined placental and fetal brain ADC used to define neonates needing ICU revealed an AUC of 0.968 and an accuracy of 93.3%.

CONCLUSION

Combined ADC of the placenta and fetal organs can detect IUGR, and combined ADC of the placenta and fetal brain can define fetuses needing ICU.

In vivo textural and morphometric analysis of placental development in healthy & growth-restricted pregnancies using magnetic resonance imaging

BACKGROUND

The objective of this study was to characterize structural changes in the healthy in vivo placenta by applying morphometric and textural analysis using magnetic resonance imaging (MRI), and to explore features that may be able to distinguish placental insufficiency in fetal growth restriction (FGR).

METHODS

Women with healthy pregnancies or pregnancies complicated by FGR underwent MRI between 20 and 40 weeks gestation. Measures of placental morphometry (volume, elongation, depth) and digital texture (voxel-wise geometric and signal-intensity analysis) were calculated from T2W MR images.

RESULTS

We studied 66 pregnant women (32 healthy controls, 34 FGR); during the study period, placentas undergo significant increases in size; signal intensity remains relatively constant, however there is increasing variation in spatial arrangements, suggestive of progressive microstructural heterogeneity. In FGR, placental size is smaller, with great homogeneity of signal intensity and spatial arrangements.

CONCLUSION

We report quantitative textural and morphometric changes in the in vivo placenta in healthy controls over the second half of pregnancy. These MRI features demonstrate important differences in placental development in the setting of placental insufficiency that relate to onset and severity of FGR, as well as neonatal outcome.

Texture analysis of magnetic resonance images of the human placenta throughout gestation: A feasibility study

As fetal gestational age increases, other modalities such as ultrasound have demonstrated increased levels of heterogeneity in the normal placenta. In this study, we introduce and apply ROI-based texture analysis to a retrospective fetal MRI database to characterize the second-order statistics of placenta and to evaluate the relationship between heterogeneity and gestational age. Positive correlations were observed for several Haralick texture metrics derived from fetal-brain specific T2-weighted and gravid uterus T1-weighted and T2-weighted images, confirming a quantitative increase in placental heterogeneity with gestational age. Our study shows the importance of identifying baseline MR textural changes at certain gestational ages from which placental diseased states may be compared. Specifically, when evaluating for placental invasion or insufficiency, findings should be evaluated in the context of the normal placental aging process, which occurs throughout gestation.

The Possible Role of Placental Morphometry in the Detection of Fetal Growth Restriction

Fetal growth restriction (FGR) is often the result of placental insufficiency and is characterized by insufficient transplacental transport of nutrients and oxygen. The main underlying entities of placental insufficiency, the pathophysiologic mechanism, can broadly be divided into impairments in blood flow and exchange capacity over the syncytiovascular membranes of the fetal placenta villi. Fetal growth restriction is not synonymous with small for gestational age and techniques to distinguish between both are needed. Placental insufficiency has significant associations with adverse pregnancy outcomes (perinatal mortality and morbidity). Even in apparently healthy survivors, altered fetal programming may lead to long-term neurodevelopmental and metabolic effects. Although the concept of fetal growth restriction is well appreciated in contemporary obstetrics, the appropriate detection of FGR remains an issue in clinical practice. Several approaches have aimed to improve detection, e.g., uniform definition of FGR, use of Doppler ultrasound profiles and use of growth trajectories by ultrasound fetal biometry. However, the role of placental morphometry (placental dimensions/shape and weight) deserves further exploration. This review article covers the clinical relevance of placental morphometry during pregnancy and at birth to help recognize fetuses who are growth restricted. The assessment has wide intra- and interindividual variability with various consequences. Previous studies have shown that a small placental surface area and low placental weight are associated with a slower growth of the fetus. Parameters such as placental surface area, placental volume and placental weight in relation to birth weight can help to identify FGR. In the future, a model including sophisticated antenatal placental morphometry may prove to be a clinically useful method for screening or diagnosing growth restricted fetuses, in order to provide optimal monitoring.

Maternal Hematological Parameters and Placental and Umbilical Cord Histopathology in Intrauterine Growth Restriction

OBJECTIVE

To investigate the placental and umbilical cord histopathology in intrauterine growth restriction (IUGR) and their relation to second-trimester maternal hematological parameters.

MATERIALS AND METHODS

Patients were selected for the IUGR group based on estimated fetal weight below the 10th percentile. Patients were recruited into the control group randomly. Patients were followed up with ultrasound, and blood samples were taken between the 20th and 24th gestational weeks. After delivery and formalin fixation, weight and volume of the placenta were recorded and histologic samples were processed.

RESULTS

Maternal platelet count strongly correlates with placental weight (r = 0.766). On the other hand, neonatal weight correlates with placental volume (r = 0.572) rather than with placental weight (r = 0.469). Umbilical arterial lumen cross-sectional area correlates with birth weight (r = 0.338).

CONCLUSIONS

Maternal hematological parameters do not seem to affect neonatal outcome. Our main findings are the correlation of maternal platelet count with placental weight, the correlation of placental volume with birth weight being stronger than the correlation of placental weight with birth weight, and the correlation of umbilical artery lumen cross-sectional area with neonatal weight. Mild histopathologic alterations might occur in normal pregnancies; however, sufficient fetal nutrition can be maintained. This compensatory function of the placenta seems to be insufficient when two or more pathologies are present, which is characteristic for IUGR.

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects

Fetal imaging is a burgeoning topic. New advancements in both magnetic resonance imaging and (3D) ultrasound currently allow doctors to diagnose fetal structural abnormalities such as those involved in twin-to-twin transfusion syndrome, gestational diabetes mellitus, pulmonary sequestration and hypoplasia, congenital heart disease, diaphragmatic hernia, ventriculomegaly, etc. Considering the continued breakthroughs in utero image analysis and (3D) reconstruction models, it is now possible to gain more insight into the ongoing development of the fetus. Best prenatal diagnosis performances rely on the conscious preparation of the clinicians in terms of fetal anatomy knowledge. Therefore, fetal imaging will likely span and increase its prevalence in the forthcoming years. This review covers state-of-the-art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time. Potential applications of the aforementioned methods into clinical settings are also inspected. Finally, improvements in existing approaches as well as most promising avenues to new areas of research are briefly outlined.