Second-trimester fetal head circumference in more than 350 000 pregnancies: Outcome and suggestion for sex-dependent cutoffs for small heads

Prenatal evaluation of genetic variants in fetuses with small head circumference: A single-center retrospective study

OBJECTIVES

To comprehensively evaluate the contributions of numerical chromosomal abnormality, copy number variant (CNV), and sequence variant (SV) to fetuses with small head circumference in a Chinese cohort using chromosome microarray analysis and whole exome sequencing.

METHODS

A total of 157 fetuses with small heads defined as head circumference < - 2 standard deviation (SD) were recruited between October 2014 and March 2023. We used the ultrasonic measurement parameter Z-score to define small head as possible microcephaly (3 < Z ≤ -2), microcephaly (-5 < Z ≤ -3), or pathologic microcephaly (Z ≤ -5). Ultrasound findings and genetic results were analyzed.

RESULTS

The overall diagnostic yield of chromosomal abnormalities by microarray analysis was 13 %. Whole exome sequencing revealed eight novel variants and two interesting candidate genes and provided a 25.4 % incremental yield compared with microarray analysis. Of the detected SVs, 56 % were de novo and the most common inheritance pattern was autosomal dominant inheritance presented in 11/16 fetuses. Compared with isolated small heads, non-isolated small heads had a significantly higher detection rate of chromosomal abnormalities (16 % vs. 3.0 %, P = 0.049) but not SVs (24 % vs. 5.5 %, P = 0.126). Subgroup analysis showed that intracranial anomalies had a similar high detection rate of SVs in fetuses with all small heads subgroups while no chromosomal abnormalities and causative SVs were found in fetuses with isolated possible microcephaly.

CONCLUSIONS

Ultrasound findings of small fetal head circumference < 3 SD below the mean, especially those with intracranial structural abnormalities, indicate the need for genetic counseling. Genetic variants, mainly copy number variants and SV, may be responsible for the substantial proportion of small fetal head circumference, while most are de novo. Whole exome sequencing and microarray analysis are effective diagnostic approaches for this population.

Size at birth predicts later brain volumes

We aimed to investigate whether gestation at birth, birth weight, and head circumference at birth are still associated with brain volume and white matter microstructure at 9-10 years in children born late-preterm and at term. One hundred and eleven children born at ≥ 36 weeks gestation from the CHYLD Study cohort underwent brain magnetic resonance imaging at 9 to 10 years. Images were analysed using FreeSurfer for volumetric data and tract-based spatial statistics for diffusion data. Of the cohort, 101 children were included for volumetric analysis [boys, 49(49%); median age, 9.5 (range: 8.9-12.4) years]. Shorter gestation at birth, lower birthweight, and smaller birth head circumference were associated with smaller brain volumes at 9 to 10 years, both globally and regionally. Amongst the perinatal factors studied, head circumference at birth was the strongest predictor of later brain volumes. Gestation at birth and absolute birthweight were not associated with diffusion metrics of white matter skeleton. However, lower birthweight z-score was associated with higher fractional anisotropy and lower radial diffusivity. Our findings suggest that even in children born late preterm and at term, growth before birth and timing of birth are still associated with brain development in mid-childhood.

A new approach to automatic measure fetal head circumference in ultrasound images using convolutional neural networks

Fetal head circumference (HC) is an important biological index in prenatal ultrasound screening. In the clinic, fetal HC is usually measured manually by sonographers in two dimensional (2D) ultrasound images. The manual method is significantly affected by the inter/intra-observer difference and the process of manual measurement is inconvenient and time-consuming for sonographers. Although several artificial intelligence (AI) approaches had been applied to fetal HC measurement, they had weak generalization ability, especially for the incomplete or blurred skull edge. In this study, a fast and accurate method for fetal HC auto-measurement was proposed. Different from the common region segmentation method, an end-to-end convolutional neural network (CNN) for fetal skull boundary segmentation in 2D ultrasound images is proposed, which is an efficient method to directly segment the boundary of fetal skull by using the proposed double-branch structure. The segmentation results can be directly used to calculate fetal HC without complex post-processing. The proposed approach achieved excellent results: Mean Dice Sore (MDS)±std: 97.98 ± 1.30, Mean Hausdorff Distance (MHD)±std: 1.20 ± 0.68 mm, Mean Absolute Difference (MAD)±std: 1.75 ± 1.60 mm, Mean Difference (MD)±std: 0.08 ± 2.37 mm. Additionally, we drew a Bland-Altman plot to demonstrate that HC measured by the proposed approach has high agreement with the real value. Comprehensive results show that the proposed approach is comparable to the state-of-the-art methods for fetal HC measurement. Meanwhile, our approach belongs to a lightweight network with less parameters, which is convenient for deployment. We hope it could provide help for precision medicine in prenatal ultrasound screening.

Crown-rump length measurement error: impact on assessment of growth

OBJECTIVE

To examine the impact of first-trimester crown-rump length (CRL) measurement error on the interpretation of estimated fetal weight (EFW) and classification of fetuses as small-, large- or appropriate-for-gestational age on subsequent growth scans.

METHODS

We examined the effects of errors of ± 2, ± 3 and ± 4 mm in the measurement of fetal CRL on percentiles of EFW at 20, 32 and 36 weeks' gestation and classification as small-, large- or appropriate-for-gestational age. Published data on CRL measurement error were used to determine variation present in practice.

RESULTS

A measurement error of -2 mm in first-trimester CRL shifts an EFW on the 10^th percentile at the 20-week scan to around the 20^th percentile, and the effect of a CRL measurement error of + 2 mm would shift an EFW on the 10^th percentile to around the 5^th percentile. At 32 weeks, a first-trimester CRL measurement error would shift an EFW on the 10^th percentile to the 7^th (+ 2 mm) or 14^th (-2 mm) percentile; at 36 weeks, the EFW would shift from the 10^th percentile to the 8^th (+ 2 mm) or 12^th (-2 mm) percentile. Published data suggest that measurement errors of 2 mm or more are common in practice.

CONCLUSION

Because of the widespread and potentially severe consequences of CRL measurement errors as small as 2 mm on clinical assessment, patient management and research results, there is a need to increase awareness of the impact of CRL measurement error and to reduce measurement error variation through standardization and quality control. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Association of gestational age with MRI-based biometrics of brain development in fetuses

Background

Reported date of last menstrual period and ultrasonography measurements are the most commonly used methods for determining gestational age in antenatal life. However, the mother cannot always determine the last menstrual period with certainty, and ultrasonography measurements are accurate only in the first trimester. We aimed to assess the ability of various biometric measurements on magnetic resonance imaging (MRI) in determining the accurate gestational age of an individual fetus in the second half of gestation.

Methods

We used MRI to scan a total of 637 fetuses ranging in age from 22 to 40 gestational weeks. We evaluated 9 standard fetal 2D biometric parameters, and regression models were fitted to assess normal fetal brain development. A stepwise linear regression model was constructed to predict gestational age, and measurement accuracy was determined in a held-out, unseen test sample (n = 49).

Results

A second-order polynomial regression model was found to be the best descriptor of biometric measures including brain bi-parietal diameter, head circumference, and fronto-occipital diameter in relation to normal fetal growth. Normal fetuses showed divergent growth patterns for the cerebrum and cerebellum, where the cerebrum undergoes rapid growth in the second trimester, while the cerebellum undergoes rapid growth in the third trimester. Moreover, a linear model based on biometrics of brain bi-parietal diameter, length of the corpus callosum, vermis area, transverse cerebellar diameter, and cerebellar area accurately predicted gestational age in the second and third trimesters (cross-validation R² = 0.822, p < 0.001).

Conclusions

These results support the use of MRI biometry charts to improve MRI evaluation of fetal growth and suggest that MRI biometry measurements offer a potential estimation model of fetal gestational age in the second half of gestation, which is vital to any assessment of pregnancy, fetal development, and neonatal care.