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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 accurat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689975/ https://www.ncbi.nlm.nih.gov/pubmed/33238909 http://dx.doi.org/10.1186/s12880-020-00525-9 |
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author | Shi, Yuequan Xue, Yunjing Chen, Chunxia Lin, Kaiwu Zhou, Zuofu |
author_facet | Shi, Yuequan Xue, Yunjing Chen, Chunxia Lin, Kaiwu Zhou, Zuofu |
author_sort | Shi, Yuequan |
collection | PubMed |
description | 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 R(2) = 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. |
format | Online Article Text |
id | pubmed-7689975 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-76899752020-11-30 Association of gestational age with MRI-based biometrics of brain development in fetuses Shi, Yuequan Xue, Yunjing Chen, Chunxia Lin, Kaiwu Zhou, Zuofu BMC Med Imaging Research Article 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 R(2) = 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. BioMed Central 2020-11-25 /pmc/articles/PMC7689975/ /pubmed/33238909 http://dx.doi.org/10.1186/s12880-020-00525-9 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Shi, Yuequan Xue, Yunjing Chen, Chunxia Lin, Kaiwu Zhou, Zuofu Association of gestational age with MRI-based biometrics of brain development in fetuses |
title | Association of gestational age with MRI-based biometrics of brain development in fetuses |
title_full | Association of gestational age with MRI-based biometrics of brain development in fetuses |
title_fullStr | Association of gestational age with MRI-based biometrics of brain development in fetuses |
title_full_unstemmed | Association of gestational age with MRI-based biometrics of brain development in fetuses |
title_short | Association of gestational age with MRI-based biometrics of brain development in fetuses |
title_sort | association of gestational age with mri-based biometrics of brain development in fetuses |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689975/ https://www.ncbi.nlm.nih.gov/pubmed/33238909 http://dx.doi.org/10.1186/s12880-020-00525-9 |
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