The texture analysis of MRI diffusion-weighted imaging for predicting prognosis of neonatal hypoglycemic encephalopathy / 中华全科医师杂志

ABSTRACT

Objective:To investigate the prognostic value of texture analysis of MRI diffusion weighted imaging (DWI) for neonatal hypoglycemic encephalopathy (HE).Methods:The clinical data and MRI data of 119 patients with neonatal HE admitted to Children′s Hospital of Nanjing Medical University from July 2013 to September 2020 were retrospectively analyzed. The children were followed up to 7—8 months and scored by Bayley scales of infant and toddler development. According to the overall development index, the children were divided into three groups: normal group (≥85, group A, n=42), mild developmental retardation group (70－84, group B, n=46) and developmental retardation group (≤69, group C, n= 31). The whole brain region (except sulcus and cisterna) was delineated as region of interest (ROI) by LIFEx 3.4 software in MRI apparent diffusion coefficient images. A total of 37 parameters were calculated automatically by the software, The clinical data, including gender, gestational age, age at MRI scan, birth weight, mode of delivery, history of asphyxia at birth, maternal preeclampsia or diabetes, minimum blood glucose, duration of hypoglycemia, neonatal behavioral neurological assessment (NBNA), presence or absence of polycythemia); the texture parameters, including histogram, volume, gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray tone difference matrix (NGTDM), gray level size zone matrix (GLSZM), in the three groups were analyzed; and the diagnostic efficacy of clinical parameters and texture parameters was analyzed. Multivariate Logistic regression was used to analyze statistically significant clinical parameters and texture parameters, and receiver operating characteristic curve (ROC) was used to evaluate the prognostic efficacy of these parameter for neonatal HE. Results:There were no significant differences in gender, gestational age, age at MRI scan, delivery mode and blood glucose minimum among the three groups ( P>0.05). There were significant differences in birth weight [(3 150±130)g, (3 020±220)g, (2 880±140)g, F=-0.31, P=0.015], history of suffocation (10 cases, 18 cases, 20 cases, P=0.001), history of maternal diabetes or preeclampsia (14 cases, 29 cases, 21 cases, P=0.002), blood glucose duration [(5.0±0.2)d, (8.0±0.4)d, (14.0±1.7)d, F=-3.09, P=0.030] and NBNA scores (32.0±3.2, 28.0±2.6, 22.0±1.9, F=-4.21, P=0.010) among three groups. There were significant differences in kurtosis and entropy of histogram (2.57±1.12, 3.66±0.98, 4.23±0.37, F=3.54, P=0.010;5.89±1.09, 7.67±2.12, 8.92±1.62, F=-4.42, P=0.020); energy, contrast and dissimilarity of GLCM (0.48±0.01, 0.36±0.02, 0.23±0.01, F=-3.12, P=0.001;2 419±21, 3 354±31, 4 313±26, F=-4.16, P=0.020;126±14, 153±23, 344±43, F=-3.50, P<0.001); long run emphasis of GLRLM (0.78±0.15, 1.12±0.12, 1.76±0.31, F=-4.13, P=0.006), run length non-uniformity and run percentage (71.7±13.9, 96.6±10.7, 104.1±13.5, F=-0.98, P=0.001;0.91±0.05, 0.84±0.21, 0.72±0.17, F=2.97, P=0.010); coarseness and busyness of NGTDM [0.09±0.01, 0.13±0.03, 0.26±0.07, F=-1.95, P=0.003;0.16(0.04, 4.14), 0.32(0.05, 9.84), 0.45(0.15, 10.14), H=-3.24, P=0.030], short-zone emphasis and short-zone high gray length emphasis of GLSZM (4.74±0.45, 3.44±1.03, 1.88±0.67, F=-3.14, P=0.040; 278 963±239, 164 607±544, 111 653±618, F=-3.84, P=0.001) among three groups. Multivariate Logistic regression showed that duration of hypoglycemia, NBNA score, energy, kurtosis, run percentage and short zone effect were independent risk factors for poor prognosis of neonatal HE ( OR=7.43, 4.09, 1.10, 2.11, 1.36, 1.68, P=0.002, 0.027, 0.001, 0.006, 0.007, 0.010, respectively). ROC curve showed that for combined hypoglycemic duration, NBNA and texture parameters, the area under the curve (AUC) was the highest (AUC=0.94, P<0.001). Conclusion:Texture analysis of the MRI diffusion weighted imaging can predict the prognosis of neonatal hypoglycemic encephalopathy at an early stage, which has better prediction efficiency when combined with clinical features.