1. Diagnostic yield of prenatal exome sequencing in the genetic screening of fetuses with brain anomalies detected by MRI and ultrasonography: A systematic review and meta‐analysis.
- Author
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Moradi, Behnaz, Ariaei, Armin, Heidari‐Foroozan, Mahsa, Banihashemian, Masoumeh, Ghorani, Hamed, Rashidi‐Nezhad, Ali, Kazemi, Mohammad Ali, and Taheri, Morteza Sanei
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MAGNETIC resonance imaging , *CENTRAL nervous system , *GENETIC testing , *PRENATAL diagnosis , *CONGENITAL disorders - Abstract
Background: Brain anomalies (BAs) have been the focus of research, as they have a high impact on fetal health but therapeutic and diagnostic approaches are limited. Objectives: In this study, the application and efficiency of exome sequencing (ES) in detecting different cases of BAs in fetuses were evaluated and compared with chromosomal microarray analysis (CMA). Search strategy: To conduct this study, three databases including PubMed, Web of Science and Embase were utilised with the keywords 'prenatal', 'diagnoses', 'brain anomalies' and 'exome sequencing'. Selection criteria: Studies were included based on the STARD checklist, for which the ES and CMA diagnostic yields were calculated. Data collection and analysis: Meta‐analysis was performed on the included studies using a random‐effects model and subgroup analysis to define the risk difference between them. Main results: We included 11 studies representing 779 fetuses that implemented ES along with imaging techniques. The pooled ES diagnostic yield in fetuses with BAs detected through magnetic resonance imaging (MRI) and ultrasonography was 26.53%, compared with 3.46% for CMA. The risk difference between ES and CMA for complex BAs was 0.36 [95% confidence interval (CI) 0.24–0.47], which was higher than for single BAs (0.22; 95% CI 0.18–0.25]. Conclusions: ES is a useful method with a significantly higher diagnostic yield than CMA for genetic assessment of fetuses with complex BAs detected by imaging techniques. Moreover, ES could be applied to suspected fetuses with related family histories to predict congenital diseases with high efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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