The advantages of the magnetic resonance image compilation (MAGiC) method for the prognosis of neonatal hypoglycemic encephalopathy
ObjectivesTo explore the prognostic value of magnetic resonance image compilation (MAGiC) in the quantitative assessment of neonatal hypoglycemic encephalopathy (HE).MethodsA total of 75 neonatal HE patients who underwent synthetic MRI were included in this retrospective study. Perinatal clinical da...
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1179535/full |
| _version_ | 1827925621130395648 |
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| author | Zhongfu Tian Qing Zhu Ruizhu Wang Yanli Xi Wenwei Tang Ming Yang |
| author_facet | Zhongfu Tian Qing Zhu Ruizhu Wang Yanli Xi Wenwei Tang Ming Yang |
| author_sort | Zhongfu Tian |
| collection | DOAJ |
| description | ObjectivesTo explore the prognostic value of magnetic resonance image compilation (MAGiC) in the quantitative assessment of neonatal hypoglycemic encephalopathy (HE).MethodsA total of 75 neonatal HE patients who underwent synthetic MRI were included in this retrospective study. Perinatal clinical data were collected. T1, T2 and proton density (PD) values were measured in the white matter of the frontal lobe, parietal lobe, temporal lobe and occipital lobe, centrum semiovale, periventricular white matter, thalamus, lenticular nucleus, caudate nucleus, corpus callosum and cerebellum, which were generated by MAGiC. The patients were divided into two groups (group A: normal and mild developmental disability; group B: severe developmental disability) according to the score of Bayley Scales of Infant Development (Bayley III) at 9–12 months of age. Student’s t test, Wilcoxon test, and Fisher’s test were performed to compare data across the two groups. Multivariate logistic regression was used to identify the predictors of poor prognosis, and receiver operating characteristic (ROC) curves were created to evaluate the diagnostic accuracy.ResultsT1 and T2 values of the parietal lobe, occipital lobe, center semiovale, periventricular white matter, thalamus, and corpus callosum were higher in group B than in group A (p < 0.05). PD values of the occipital lobe, center semiovale, thalamus, and corpus callosum were higher in group B than in group A (p < 0.05). Multivariate logistic regression analysis showed that the duration of hypoglycemia, neonatal behavioral neurological assessment (NBNA) scores, T1 and T2 values of the occipital lobe, and T1 values of the corpus callosum and thalamus were independent predictors of severe HE (OR > 1, p < 0.05). The T2 values of the occipital lobe showed the best diagnostic performance, with an AUC value of 0.844, sensitivity of 83.02%, and specificity of 88.16%. Furthermore, the combination of MAGiC quantitative values and perinatal clinical features can improve the AUC (AUC = 0.923) compared with the use of MAGiC or perinatal clinical features alone.ConclusionThe quantitative values of MAGiC can predict the prognosis of HE early, and the prediction efficiency is further optimized after being combined with clinical features. |
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| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-13T05:26:41Z |
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| spelling | doaj.art-a8ad08f6d4974befaddbcb0e28d18adb2023-06-15T05:29:37ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-06-011710.3389/fnins.2023.11795351179535The advantages of the magnetic resonance image compilation (MAGiC) method for the prognosis of neonatal hypoglycemic encephalopathyZhongfu Tian0Qing Zhu1Ruizhu Wang2Yanli Xi3Wenwei Tang4Ming Yang5Department of Radiology, Women's Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing, ChinaDepartment of Radiology, Women's Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing, ChinaDepartment of Radiology, Children’s Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Radiology, Children’s Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Radiology, Women's Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing, ChinaDepartment of Radiology, Children’s Hospital of Nanjing Medical University, Nanjing, ChinaObjectivesTo explore the prognostic value of magnetic resonance image compilation (MAGiC) in the quantitative assessment of neonatal hypoglycemic encephalopathy (HE).MethodsA total of 75 neonatal HE patients who underwent synthetic MRI were included in this retrospective study. Perinatal clinical data were collected. T1, T2 and proton density (PD) values were measured in the white matter of the frontal lobe, parietal lobe, temporal lobe and occipital lobe, centrum semiovale, periventricular white matter, thalamus, lenticular nucleus, caudate nucleus, corpus callosum and cerebellum, which were generated by MAGiC. The patients were divided into two groups (group A: normal and mild developmental disability; group B: severe developmental disability) according to the score of Bayley Scales of Infant Development (Bayley III) at 9–12 months of age. Student’s t test, Wilcoxon test, and Fisher’s test were performed to compare data across the two groups. Multivariate logistic regression was used to identify the predictors of poor prognosis, and receiver operating characteristic (ROC) curves were created to evaluate the diagnostic accuracy.ResultsT1 and T2 values of the parietal lobe, occipital lobe, center semiovale, periventricular white matter, thalamus, and corpus callosum were higher in group B than in group A (p < 0.05). PD values of the occipital lobe, center semiovale, thalamus, and corpus callosum were higher in group B than in group A (p < 0.05). Multivariate logistic regression analysis showed that the duration of hypoglycemia, neonatal behavioral neurological assessment (NBNA) scores, T1 and T2 values of the occipital lobe, and T1 values of the corpus callosum and thalamus were independent predictors of severe HE (OR > 1, p < 0.05). The T2 values of the occipital lobe showed the best diagnostic performance, with an AUC value of 0.844, sensitivity of 83.02%, and specificity of 88.16%. Furthermore, the combination of MAGiC quantitative values and perinatal clinical features can improve the AUC (AUC = 0.923) compared with the use of MAGiC or perinatal clinical features alone.ConclusionThe quantitative values of MAGiC can predict the prognosis of HE early, and the prediction efficiency is further optimized after being combined with clinical features.https://www.frontiersin.org/articles/10.3389/fnins.2023.1179535/fullsynthetic MRImagnetic resonance image compilation (MAGiC)hypoglycemic encephalopathyneonatalprognosis |
| spellingShingle | Zhongfu Tian Qing Zhu Ruizhu Wang Yanli Xi Wenwei Tang Ming Yang The advantages of the magnetic resonance image compilation (MAGiC) method for the prognosis of neonatal hypoglycemic encephalopathy Frontiers in Neuroscience synthetic MRI magnetic resonance image compilation (MAGiC) hypoglycemic encephalopathy neonatal prognosis |
| title | The advantages of the magnetic resonance image compilation (MAGiC) method for the prognosis of neonatal hypoglycemic encephalopathy |
| title_full | The advantages of the magnetic resonance image compilation (MAGiC) method for the prognosis of neonatal hypoglycemic encephalopathy |
| title_fullStr | The advantages of the magnetic resonance image compilation (MAGiC) method for the prognosis of neonatal hypoglycemic encephalopathy |
| title_full_unstemmed | The advantages of the magnetic resonance image compilation (MAGiC) method for the prognosis of neonatal hypoglycemic encephalopathy |
| title_short | The advantages of the magnetic resonance image compilation (MAGiC) method for the prognosis of neonatal hypoglycemic encephalopathy |
| title_sort | advantages of the magnetic resonance image compilation magic method for the prognosis of neonatal hypoglycemic encephalopathy |
| topic | synthetic MRI magnetic resonance image compilation (MAGiC) hypoglycemic encephalopathy neonatal prognosis |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1179535/full |
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