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PurposeTo examine the diagnostic significance of the apparent diffusion coefficient (ADC) histogram in quantifying neonatal hypoxic ischemic encephalopathy (HIE).MethodsAn analysis was conducted on the MRI data of 90 HIE patients, 49 in the moderate-to-severe group, and the other in the mild group. The 3D Slicer software was adopted to delineate the whole brain region as the region of interest, and 22 ADC histogram parameters were obtained. The interobserver consistency of the two radiologists was assessed by the interclass correlation coefficient (ICC). The difference in parameters (ICC > 0.80) between the two groups was compared by performing the independent sample t-test or the Mann–Whitney U test. In addition, an investigation was conducted on the correlation between parameters and the neonatal behavioral neurological assessment (NBNA) score. The ROC curve was adopted to assess the efficacy of the respective significant parameters. Furthermore, the binary logistic regression was employed to screen out the independent risk factors for determining the severity of HIE.ResultsThe ADCmean, ADCmin, ADCmax,10th−70th, 90th percentile of ADC values of the moderate-to-severe group were smaller than those of the mild group, while the group's variance, skewness, kurtosis, heterogeneity, and mode-value were higher than those of the mild group (P < 0.05). All the mentioned parameters, the ADCmean, ADCmin, and 10th−70th and 90th percentile of ADC displayed positive correlations with the NBNA score, mode-value and ADCmax displayed no correlations with the NBNA score, the rest showed negative correlations with the NBNA score (P < 0.05). The area under the curve (AUC) of variance was the largest (AUC = 0.977; cut-off 972.5, sensitivity 95.1%; specificity 87.8%). According to the logistic regression analysis, skewness, kurtosis, variance, and heterogeneity were independent risk factors for determining the severity of HIE (OR > 1, P < 0.05).ConclusionsThe ADC histogram contributes to the HIE diagnosis and is capable of indicating the diffusion information of the brain objectively and quantitatively. It refers to a vital method for assessing the severity of HIE.

In order to evaluate brain changes in young children with Pierre Robin sequence (PRs) using machine learning based on apparent diffusion coefficient (ADC) features, we retrospectively enrolled a total of 60 cases (42 in the training dataset and 18 in the testing dataset) which included 30 PRs and 30 controls from the Children's Hospital Affiliated to the Nanjing Medical University from January 2017–December 2019. There were 21 and nine PRs cases in each dataset, with the remainder belonging to the control group in the same age range. A total of 105 ADC features were extracted from magnetic resonance imaging (MRI) data. Features were pruned using least absolute shrinkage and selection operator (LASSO) regression and seven ADC features were developed as the optimal signatures for training machine learning models. Support vector machine (SVM) achieved an area under the receiver operating characteristic curve (AUC) of 0.99 for the training set and 0.85 for the testing set. The AUC of the multivariable logistic regression (MLR) and the AdaBoost for the training and validation dataset were 0.98/0.84 and 0.94/0.69, respectively. Based on the ADC features, the two groups of cases (i.e., the PRs group and the control group) could be well-distinguished by the machine learning models, indicating that there is a significant difference in brain development between children with PRs and normal controls.

Apparent diffusion coefficients (ADC) can provide phenotypic information of brain lesions, which can aid the diagnosis of brain alterations in neonates with congenital heart diseases (CHDs). However, the corresponding clinical significance of quantitative descriptors of brain tissue remains to be elucidated. By using ADC metrics and texture features, this study aimed to investigate the diagnostic value of single-slice and multi-slice measurements for assessing brain alterations in neonates with CHDs. ADC images were acquired from 60 neonates with echocardiographically confirmed non-cyanotic CHDs and 22 healthy controls (HCs) treated at Children's Hospital of Nanjing Medical University from 2012 to 2016. ADC metrics and texture features for both single and multiple slices of the whole brain were extracted and analyzed to the gestational age. The diagnostic performance of ADC metrics for CHDs was evaluated by using analysis of covariance and receiver operating characteristic. For both the CHD and HC groups, ADC metrics were inversely correlated with the gestational age in single and multi-slice measurements (P < 0.05). Histogram metrics were significant for identifying CHDs (P < 0.05), while textural features were insignificant. Multi-slice ADC (P < 0.01) exhibited greater diagnostic performance for CHDs than single-slice ADC (P < 0.05). These findings indicate that radiomic analysis based on ADC metrics can objectively provide more quantitative information regarding brain development in neonates with CHDs. ADC metrics for the whole brain may be more clinically significant in identifying atypical brain development in these patients. Of note, these results suggest that multi-slice ADC can achieve better diagnostic performance for CHD than single-slice.

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