Ultrasound and fetal magnetic resonance imaging: Clinical performance in the prenatal diagnosis of orofacial clefts and mandibular abnormalities

Fetal magnetic resonance imaging in the confirmation of congenital anomalies found on routine mid-trimester ultrasound

OBJECTIVE

To determine the added value of fetal magnetic resonance imaging (MRI) when clarifying a suspected anomaly detected by mid-trimester scan.

METHODS

Women attending two centers of fetal medicine between January 2017 and December 2021 were identified. The centers carried out routine mid-trimester ultrasound scans to detect fetal anomalies. Those with a suspected anomaly which required further clarification were referred for fetal magnetic resonance imaging (MRI). The medical records of all referred women were examined to determine the anomalies found at scan, MRI and termination of pregnancy or delivery. A total of 9571 women had a routine mid-trimester scan and an anomaly was either diagnosed or suspected in 449 (4.7%); an MRI examination was made in 76 cases (0.79%).

RESULTS

MRI confirmed the presence of an abnormality in 61 referrals (80%) and failed to yield a result in one case. Outcome information was available for 69 cases: the MRI confirmation rate was 89% (48/54) in those with abnormal outcome and 40% (6/15) if the outcome was normal, P.

Chromosomal Abnormalities Detected by Chromosomal Microarray Analysis and Karyotype in Fetuses with Ultrasound Abnormalities

Objective

Chromosomal microarray analysis (CMA) is a first-line test to assess the genetic etiology of fetal ultrasound abnormalities. The aim of this study was to evaluate the effectiveness of CMA in detecting chromosomal abnormalities in fetuses with ultrasound abnormalities, including structural abnormalities and non-structural abnormalities.

Methods

A retrospective study was conducted on 368 fetuses with abnormal ultrasound who received interventional prenatal diagnosis at Meizhou People's Hospital from October 2022 to December 2023. Samples of villi, amniotic fluid, and umbilical cord blood were collected according to different gestational weeks, and karyotype and CMA analyses were performed. The detection rate of chromosomal abnormalities in different ultrasonic abnormalities was analyzed.

Results

There were 368 fetuses with abnormal ultrasound, including 114 (31.0%) with structural abnormalities, 225 (61.1%) with non-structural abnormalities, and 29 (7.9%) with structural combined with non-structural abnormalities. The detection rate of aneuploidy and pathogenic (P)/likely pathogenic (LP) copy number variations (CNVs) of CMA in fetuses with structural abnormalities was 5.26% (6/114), the detection rate of karyotype was 2.63% (3/114), and the additional diagnosis rate of CMA was 2.63%. In the fetuses with ultrasonic non-structural abnormalities, the detection rate of karyotype was 6.22% (14/225), the detection rate of aneuploidy and P/LP CNVs in fetuses with ultrasonic structural abnormalities was 9.33% (21/225), and the additional diagnosis rate of CMA was 3.11%. There was no significant difference in chromosome abnormality detection rate of CMA among structural abnormality, non-structural abnormality, and structural abnormality combined with non-structural abnormality groups (5.3%, 9.3%, and 13.8%, p = 0.241), also among multiple ultrasonic abnormality and single ultrasonic abnormality groups (14.8%, and 7.3%, p = 0.105).

Conclusion

CMA can significantly improve the detection rate of genetic abnormalities in prenatal diagnosis of ultrasonic abnormal fetuses compared with karyotype analysis. CMA is a more effective tool than karyotyping alone in detecting chromosomal abnormalities in fetuses with ultrasound abnormalities.

Comparison of best landmarks for calculating fetal jaw measurements by ultrasound and MRI in micrognathia

BACKGROUND

Micrognathia can be diagnosed in utero with ultrasound by measuring the jaw index and/or inferior facial angle, though it can be challenging due to fetal positioning. The jaw index can be measured with magnetic resonance imaging (MRI) using the masseter muscle, but indistinct margins can lead to inaccuracy; the easily visualized posterior teeth buds may be a better landmark.

OBJECTIVE

We aimed to evaluate inter-reader variability, agreement with ultrasound, and association with postnatal outcomes using MRI to measure the inferior facial angle, jaw index by masseter muscle, and jaw index by posterior teeth buds.

MATERIALS AND METHODS

A single-institution retrospective review was performed of singleton pregnancies with prenatally diagnosed micrognathia by ultrasound or MRI from September 2013-June 2022. Ultrasound measurements were obtained by a maternal-fetal medicine specialist and MRI measurements by two radiologists to evaluate inter-reader variability. Intraclass correlation coefficients (ICC) and Bland-Altman analysis were used to assess agreement between imaging methods and logistic regressions and ROC curves to assess associations with postnatal outcomes.

RESULTS

Forty-three fetuses (median gestational age 26 weeks (IQR 22-31); 47% male (20/43)) were included. Ultrasound measurements could not be obtained for jaw index in 15/43 (35%) fetuses and inferior facial angle in 11/43 (26%); MRI measurements were obtained by at least one reader in all cases. Jaw index by teeth buds demonstrated lowest inter-reader variability (ICC = 0.82, P < 0.001) and highest agreement with ultrasound (bias -0.23, 95% CI -2.8-2.2). All MRI measurements, but not ultrasound, predicted need for mandibular distraction (inferior facial angle P = 0.02, jaw index by masseter muscle P = 0.04, jaw index by teeth buds P = 0.01).

CONCLUSION

Fetal MRI measurements, particularly jaw index measured by posterior teeth buds, demonstrate low inter-reader variability and high agreement with ultrasound, and may predict need for mandibular distraction postnatally.

Diagnosis and Early Management of Robin Sequence

The results of a survey of twenty-four neonatal units in the United Kingdom and Ireland are presented. A structured ten-item questionnaire was used, and demonstrated the variation in how infants with RS are diagnosed and managed. Notably, the survey revealed that a minority of infants were diagnosed antenatally. There were significant discrepancies in diagnostic criteria used and 79% of the units referred the patients to tertiary services. A preference for minimally invasive approaches to managing upper airway obstruction, such as a trial of prone positioning before progressing to a nasopharyngeal airway, was reported by 96% of the centers. A narrative review was undertaken which discusses the current practices for diagnosis and early management of Robin sequence (RS). The challenges of antenatal diagnosis, strategies to enhance outcomes through early detection and controversies surrounding the management of neonatal upper airway obstruction associated with RS are included. The results of the survey and our comprehensive review of the literature emphasize that there remains uncertainty regarding the best approach to treating Robin sequence.

An effective ultrasound fetal palate screening software based on the "sequential sector scan through the oral fissure" and three-dimensional ultrasound

BACKGROUND

Orofacial clefts are one of the most common congenital malformations of the fetal face and ultrasound is mainly responsible for its diagnosis. It is difficult to view the fetal palate, so there is currently no unified standard for fetal palate screening, and the diagnosis of cleft palate is not included in the relevant prenatal ultrasound screening guidelines. Many prenatal diagnoses for cleft palate are missed due to the lack of effective screening methods. Therefore, it is imperative to increase the display rate of the fetal palate, which would improve the detection rate and diagnostic accuracy for cleft palate. We aim to introduce a fetal palate screening software based on the "sequential sector scan though the oral fissure", an effective method for fetal palate screening which was verified by our follow up results and three-dimensional ultrasound and to evaluate its feasibility and clinical practicability.

METHODS

A software was designed and programmed based on "sequential sector scan through the oral fissure" and three-dimensional ultrasound. The three-dimensional ultrasound volume data of the fetal face were imported into the software. Then, the median sagittal plane was taken as the reference interface, the anterior upper margin of the mandibular alveolar bone was selected as the fulcrum, the interval angles, and the number of layers of the sector scan were set, after which the automatic scan was performed. Thus, the sector scan sequential planes of the mandibular alveolar bone, pharynx, soft palate, hard palate, and maxillary alveolar bone were obtained in sequence to display and evaluate the palate. In addition, the feasibility and accuracy of the software in fetal palate displaying and screening was evaluated by actual clinical cases.

RESULTS

Full views of the normal fetal palates and the defective parts of the cleft palates were displayed, and relatively clear sequential tomographic images and continuous dynamic videos were formed after the three-dimensional volume data of 10 normal fetal palates and 10 cleft palates were imported into the software.

CONCLUSIONS

The software can display fetal palates more directly which might allow for a new method of fetal palate screening and cleft palate diagnosis.

Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models

This study explores the potential of 3D Slice-to-Volume Registration (SVR) motion-corrected fetal MRI for craniofacial assessment, traditionally used only for fetal brain analysis. In addition, we present the first description of an automated pipeline based on 3D Attention UNet trained for 3D fetal MRI craniofacial segmentation, followed by surface refinement. Results of 3D printing of selected models are also presented.Qualitative analysis of multiplanar volumes, based on the SVR output and surface segmentations outputs, were assessed with computer and printed models, using standardised protocols that we developed for evaluating image quality and visibility of diagnostic craniofacial features. A test set of 25, postnatally confirmed, Trisomy 21 fetal cases (24-36 weeks gestational age), revealed that 3D reconstructed T2 SVR images provided 66-100% visibility of relevant craniofacial and head structures in the SVR output, and 20-100% and 60-90% anatomical visibility was seen for the baseline and refined 3D computer surface model outputs respectively. Furthermore, 12 of 25 cases, 48%, of refined surface models demonstrated good or excellent overall quality with a further 9 cases, 36%, demonstrating moderate quality to include facial, scalp and external ears. Additional 3D printing of 12 physical real-size models (20-36 weeks gestational age) revealed good/excellent overall quality in all cases and distinguishable features between healthy control cases and cases with confirmed anomalies, with only minor manual adjustments required before 3D printing.Despite varying image quality and data heterogeneity, 3D T2w SVR reconstructions and models provided sufficient resolution for the subjective characterisation of subtle craniofacial features. We also contributed a publicly accessible online 3D T2w MRI atlas of the fetal head, validated for accurate representation of normal fetal anatomy.Future research will focus on quantitative analysis, optimizing the pipeline, and exploring diagnostic, counselling, and educational applications in fetal craniofacial assessment.

Cleft Lip and Palate Antenatal Diagnosis: A Swiss University Center Performance Analysis

Precision of cleft lip and/or palate antenatal diagnosis plays a significant role in counselling, neonatal care, surgical strategies and psychological support of the family. This study aims to measure the accuracy of antenatal diagnosis in our institution and the detection rate of cleft lip and/or palate on routine morphologic ultrasonography. In this retrospective observational study, we compared antenatal and postnatal diagnosis of 233 patients followed in our unit. We classified our patients according to the Kernahan and Stark's classification system: Group 1: facial cleft including labial and labio-maxillary clefts; Group 2: facial cleft including total, subtotal and submucous palatal clefts; Group 3: labio-maxillary-palatal clefts. Out of 233 patients, 104 were antenatally diagnosed with a facial cleft, i.e., an overall detection rate of 44.6%. The diagnosis was confirmed at birth in 65 of these patients, i.e., an overall accuracy of 62.5%. Of the 67 children (29.2%) in Group 1, the screening detection rate was 58.2% with an antenatal diagnostic accuracy of 48.7%. Of the 97 children (41.6%) in Group 2, the screening detection rate was 2% with an antenatal diagnostic accuracy of 50%. Of the 69 children (29.6%) in Group 3, the screening detection rate was 91.3% with an antenatal diagnostic accuracy of 71.4%. Our study demonstrates a relatively poor diagnostic accuracy in prenatal ultrasound, where the diagnosis was inaccurate in one third to one half of patients. It showed great variability in the screening detection rate depending on the diagnostic group observed, as well as a low rate of detection of palatal clefts.

An Innovative Three-Stage Model for Prenatal Genetic Disorder Detection Based on Region-of-Interest in Fetal Ultrasound

A global survey has revealed that genetic syndromes affect approximately 8% of the population, but most genetic diagnoses are typically made after birth. Facial deformities are commonly associated with chromosomal disorders. Prenatal diagnosis through ultrasound imaging is vital for identifying abnormal fetal facial features. However, this approach faces challenges such as inconsistent diagnostic criteria and limited coverage. To address this gap, we have developed FGDS, a three-stage model that utilizes fetal ultrasound images to detect genetic disorders. Our model was trained on a dataset of 2554 images. Specifically, FGDS employs object detection technology to extract key regions and integrates disease information from each region through ensemble learning. Experimental results demonstrate that FGDS accurately recognizes the anatomical structure of the fetal face, achieving an average precision of 0.988 across all classes. In the internal test set, FGDS achieves a sensitivity of 0.753 and a specificity of 0.889. Moreover, in the external test set, FGDS outperforms mainstream deep learning models with a sensitivity of 0.768 and a specificity of 0.837. This study highlights the potential of our proposed three-stage ensemble learning model for screening fetal genetic disorders. It showcases the model's ability to enhance detection rates in clinical practice and alleviate the burden on medical professionals.