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Early Prediction of Periventricular Leukomalacia Using Quantitative Texture Analysis of Serial Cranial Ultrasound Scans in Very Preterm Infants
ARTICLE IN PRESS Ultrasound in Med. & Biol., Vol. 00, No. 00, pp. 18, 2019 Copyright © 2019 World Federation for Ultrasound in Medicine & Biology. All rights reserved. Printed in the USA. All rights reserved. 0301-5629/$ - see front matter
https://doi.org/10.1016/j.ultrasmedbio.2019.06.413
Original Contribution EARLY PREDICTION OF PERIVENTRICULAR LEUKOMALACIA USING QUANTITATIVE TEXTURE ANALYSIS OF SERIAL CRANIAL ULTRASOUND SCANS IN VERY PRETERM INFANTS AGEDPH T YE
NA JUNG,* SANG-IL SUH,* ARIM PARK,* GUN-HA KIM,y and INSEON RYOO*TAGEDEN
* Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea; and y Department of Pediatrics, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea (Received 13 March 2019; revised 21 May 2019; in final from 21 June 2019)
Abstract—We compared texture parameters of serial cranial ultrasound (cUS) images of periventricular leukomalacia (PVL) and normal periventricular echogenicity (PVE) in very preterm infants and evaluated the early predictive values of texture analysis (TA) for PVL. Ten individuals with PVL and 10 control individuals with PVE assessed with an initial cUS within 1 wk of birth and follow-up cUS at 23 and 46 wk of life were included. TA was performed on the region of interest of PVE at the parieto-occipital area on serial cUS. Opposite changes in variance were obtained between the first two cUS sessions in both groups (p = 0.017 in PVL and p = 0.005 in PVE). The variance-to-mean ratio (VMR) between the second and first cUS sessions differed (p = 0.016) and reliably stratified the groups (area under the receiver operating characteristic curve: 0.820, 95% confidence interval: 0.5871.000, sensitivity: 100%, specificity: 60%). TA of serial cUS helps to predict PVL within 3 wk of life. (E-mails: [email protected], [email protected]) © 2019 World Federation for Ultrasound in Medicine & Biology. All rights reserved. Key Words: Index of dispersion, Periventricular leukomalacia, Preterm infant, Quantitative texture analysis, Serial cranial ultrasound, Variance.
the identification of periventricular echogenicity (PVE), the prestage of PVL, is limited partially because of its intrinsic subjectivity influenced by differences in ultrasound (US) machines, acquisition settings and variability in the experience of examiners (Tenorio et al. 2014). Furthermore, PVE is often transient and resolves spontaneously without an increased risk of neurodevelopmental disabilities, though it sometimes evolves several weeks later into leukomalacia (Narchi et al. 2013). Prior studies involving both cUS and magnetic resonance imaging (MRI) suggested that PVE may be a mild form of white matter injury (Inder et al. 2003; Maalouf et al. 2001; Miller et al. 2003), that is, the non-cystic PVL variant (Khwaja and Volpe 2008). On the other hand, some reports have hypothesized that PVE could reflect normal or delayed physiological maturational processes (Leijser et al. 2009; Vansteenkiste et al. 2009). Much effort has been extended to develop a system for the quantitative analysis of cUS scans of preterm infants to overcome the limitations of US, that is, the problems of intrinsic subjectivity (Beller et al. 2016; Narchi et al. 2013; Padilla et al. 2009; Simaeys et al. 2000; Tenorio et al. 2011, 2014; Yoshizawa et al. 2009; You et al. 2015). Several studies have reported on the
INTRODUCTION The prevalence of premature birth has gradually increased, and it is known that very preterm infants (born before 33 wk of gestation) are at high risk of adverse neurodevelopmental disability (Platt et al. 2007). Periventricular leukomalacia (PVL) is a wellknown form of periventricular white matter injury, and is a characteristic pattern of brain injury in preterm infants that has been long established as a predictor of cerebral palsy (Linsell et al. 2016). A systemic review reported that the median prevalence rate of cerebral palsy after cystic PVL was 86% (Hielkema and Hadders-Algra 2016). Therefore, early prediction of PVL is important for counseling parents and for ensuring highrisk infants receive appropriate rehabilitation (Vansteenkiste et al. 2009). Periventricular leukomalacia, especially cystic PVL, is easily detectable using cranial ultrasound (cUS). However, Address correspondence to: Sang-il Suh, Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148 Guro-dong, Guro-gu, Seoul 08308, Korea. E-mails: [email protected], [email protected]
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usefulness of cUS texture analysis (TA) (Narchi et al. 2013; Tenorio et al. 2011, 2014; You et al. 2015). TA is an automated computerized method that provides an objective, quantitative assessment of lesion heterogeneity by analyzing the distribution and relationship between pixel or voxel gray levels in the image (Narchi et al. 2013). However, no published study has evaluated and compared texture features on serial cUS scans. PVL goes through changes distinct from those of normal PVE, whether PVE is the manifestation of a repair process for mild white matter injury or physiological maturation. We hypothesized that PVL would exhibit different patterns in the TA of serial cUS compared with normal PVE. Therefore, the goals of the present study were to compare PVE patterns in very preterm infants with PVL and normal PVE using quantitative TA of cUS and evaluate the feasibility of TA of serial cUS for the early prediction of PVL. METHODS The institutional review board approved this retrospective study. The requirement for patient consent for use of clinical data was waived. Participants We searched the medical database of our institution to identify infants who underwent cUS scans between April 2011 and June 2016. We included 127 participants born between 25 and 33 wk of gestation who underwent
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an initial cUS scan within 1 wk of birth and then followup cUS scans at 23 wk (second cUS) and 46 wk (third cUS) of life. Ninety-one participants with PVE on initial cUS scan were included in our study. PVE was defined as a confluent area of increased PVE at the parieto-occipital area that was bright or brighter than the choroid plexus on coronal cUS views. Seventy-one participants were excluded because of poor image quality (n = 32), sepsis or central nervous system infection (n = 21), major cerebral pathologies such as intraventricular hemorrhage grade III or IV (n = 13) and cerebral infarction (n = 1) and use of a different US machine during the follow-up period (n = 4). Finally, 20 participants were included in the study who were diagnosed with PVL (n = 10; five males and five females) and normal PVE (n = 10; eight males and two females) on the basis of follow-up brain MRI or cUS after the term-equivalent age (Fig. 1). One neuroradiologist with 17 y of experience with brain imaging, who was blinded to the clinical findings, visually assessed those images and diagnosed PVL when cavitation or cyst formation was observed in the periventricular white matter. Image acquisition All scans were performed by a neuroradiologist with 17 y of cUS experience using an iU22 US scanner (Philips Medical Systems, Bothell, WA, USA) with a sector probe (58 Hz). The operator adjusted the machine settings, including scan gain, depth and timegain compensation, to optimize the image settings for
Fig. 1. Flowchart of study groups and participants. cUS = cranial ultrasound; MRI = magnetic resonance imaging; PVE = periventricular echogenicity; PVL = periventricular leukomalacia.
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each examination. Routine images were obtained in the coronal, sagittal and parasagittal planes through the anterior fontanel. We selected coronal images that were posterior to the ventricular antrum, which contains the white matter over the occipital horn of the lateral ventricles, and downloaded them in the original Digital Imaging and Communications in Medicine format. Texture analysis of cUS For TA, regions of interest (ROIs) were manually drawn by a neuroradiologist with 4 y of experience with cUS using the freeware MaZda Version 4.5 (Institute of Electronics, Technical University of Lodz, Lodz, Poland); the neuroradiologist was blinded to clinical results. The largest possible polygonal ROIs within the PVE were placed in both hemispheres on the downloaded coronal images with care so as to minimize the partial volume averaging effect (Fig. 2). Next, TA was performed for each ROI using the MaZda program, and we conducted normalization by remapping an image histogram within three standard deviations below and above the mean to reduce the dependency of higher-order parameters on first-order gray-level distribution and to correct technical intra- and interscanner fluctuations, as was done previously in similar investigations (Mannil et al. 2017; Meyer et al. 2017; Orphanidou-Vlachou et al. 2014; Szczypinski et al. 2009). Texture analysis yielded 308 features and we added an index of dispersion known as the variance-to-mean ratio (VMR). This measurement was used to determine the
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clustering or dispersion of luminance and to quantify the volatility (difference) in each ROI (Gentillon et al. 2016). Statistical analyses Comparisons of clinical data between the PVL and normal PVE groups were made using Fisher’s exact test for categorical variables and the independent t-test or MannWhitney U-test for continuous variables. We compared the differences in texture parameters for each period in each group using the Wilcoxon signed-rank test. Then, a comparative analysis was carried out between the two groups using the MannWhitney U-test. We conducted receiver operating characteristic (ROC) curve analysis. All parametric data are expressed in the format of mean (standard deviation). A p value of 0.05 was considered to indicate statistical significance. Statistical analyses were executed using SPSS Version 21.0 (IBM Corp., Armonk, NY, USA). RESULTS Characteristics of participants We evaluated clinical data including sex, gestational age, birth weight, Apgar score at 1 and 5 min, respiratory distress syndrome, ventilation care, and the times of the first, second and third cUS scans. Characteristics of the two groups are summarized in Table 1 and there were no significant differences the groups (all p values >0.05). Sixteen participants were evaluated during a relatively long-term follow-up period; mean = 35.0 mo (range: 2161 mo) for 8 participants with PVL and 36.0 mo (range: 2164 mo) for 8 participants with normal PVE. Cerebral palsy was confirmed in 6 participants (6/8, 75%) with PVL.
Table 1. Clinical characteristics of participants with PVL and normal PVE
Fig. 2. Delineation of regions of interest for quantitative texture analysis of periventricular echogenicity in both hemispheres on coronal cranial ultrasound image.
Characteristic
PVL (n = 10)
Normal PVE (n = 10)
p Value
Sex (male:female) Gestational age Birth weight (g) Apgar score 1 min 5 min RDS (n) Ventilation care (n) Cranial ultrasound (d) First Second Third
5:5 30 wk + 2 d (2.6) 1416 (434.7)
8:2 29 wk + 3 d (2.6) 1446 (425.8)
0.35 0.52 0.88
4.3 (1.95) 6.9 (2.33) 8 9
5.2 (1.40) 7.5 (1.4) 6 8
0.25 0.49 0.34 0.54
4.6 (2.59) 15.7 (3.40) 35.0 (5.77)
3.7 (2.63) 18.6 (3.47) 35.2 (4.69)
0.45 0.75 0.93
PVE = periventricular echogenicity; PVL = periventricular leukomalacia; RDS = respiratory distress syndrome. *Unless otherwise indicated, data are presented as the mean (standard deviation).
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Comparison of changes in the texture parameters for each period in each group We evaluated differences in all texture parameters between the first and second, second and third and first and third cUS scans for each group. Many texture parameters exhibited significant changes in each group; common parameters exhibiting significant changes in both groups are outlined in Supplementary Table S1 (online only). The variance between the first and second cUS scans exhibited opposite trends in the two groups, and the difference was statistically significant (p = 0.017 in PVL and p = 0.005 in PVE). Variance values increased between the first and second cUS scans in the PVL group, whereas values in the normal PVE group decreased in the same period (Figs. 3, 4). Comparison of texture parameters of serial cUS scans between PVL and normal PVE groups We compared texture parameters at each cUS and the ratio of change in parameters during each period between the PVL and normal PVE groups, respectively, as follows: first and second scans and ratio of second to first (R21), ratio of third to first (R31) and ratio of third to second (R32) cUS scans. Many parameters differed significantly between the two groups in terms of the second scan, R21, and R31 (all p values <0.05). Among them, texture parameters of the second cUS and R21 are summarized in Supplementary Table S2 (online only). R21 and R31 for both variance and VMR differed significantly between the two groups (all p values <0.016). However, there were no significant differences between the two groups in texture parameters for the first scan and the ratio of third to second cUS scans. ROC analysis for early prediction of PVL We performed ROC analysis to compare the areas under the ROC curves (AUCs) for texture parameters in the second cUS scan and values for R21 that differed significantly between the PVL and normal PVE groups. We did not consider values of R31 because the aim of this study was the early prediction of PVL, and the use of parameters from the third cUS scan did not fit this purpose. R21 of VMR, Perc. 99% and MaxNorm were significant parameters in ROC analysis (Fig. 5), with R21 of VMR being the most accurate diagnostic parameter for the discrimination of PVL from normal PVE (AUC = 0.820, 95% confidence interval [CI]: 0.587 1.000, p = 0.016) compared with other parameters (AUC = 0.770, 95% CI: 0.5500.990, p = 0.041 for Perc. 99%, and AUC = 0.760; 95% CI: 0.5390.981, p = 0.049 for MaxNorm). There was no significant parameter associated with the second cUS scan in ROC analysis.
Fig. 3. Plotting of variance values for serial cranial ultrasound scans in all participants with periventricular leukomalacia (a) and normal periventricular echogenicity (b). Values of variance between the first and second cranial ultrasound scans increased in the periventricular leukomalacia group (a) and decreased in the normal periventricular echogenicity group during same period. The ratio of second to first cranial ultrasound scans in terms of variance significantly differed between the two groups (p < 0.007). *The black dotted line is the trend line for the average value in each group, respectively.
We established a cutoff value of 0.842 to maximize sensitivity of the R21 of VMR, with a sensitivity of 100% and a specificity of 60%. Positive and negative predictive values were calculated by binarizing R21 of VMR with a cutoff value of 0.842. R21 of VMR had a positive predictive value of 71.5% and a negative predictive value of 100%. DISCUSSION To the best of our knowledge, this study is the first to evaluate quantitatively serial cUS scans of infants born very preterm for the purpose of early PVL prediction. Changes in variance between the first and second cUS scans were statistically significant in both the PVL
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Fig. 4. Representative cases of PVL (a) and of normal PVE (b). (a) PVL: On a coronal view of the second cUS scan (aii), the area of PVE is slightly larger, the echo range is wider and the coarseness is increased compared with the first cUS scan (ai). Also, texture analysis revealed that PVE variance increased between the first and second cUS scans (value of variance: 1048.5 in first; 2010.5 in second; and 1969.9 in third cUS scans, respectively). The third cUS scan (aiii) revealed cystic evolution of PVE, and oblique coronal T2-weighted imaging at 5 mo of age (aiv) revealed undulating ventricular borders secondary to periventricular white matter volume loss. (b) Normal PVE: The second cUS (bii) is marked by the increased internal echogenicity of periventricular white matter; however, the coarseness of the echogenicity was decreased compared with the first cUS scan (bi). Texture analysis reveals declining variance between the first and second cUS scans and the first and third cUS scans (biii) (value of variance: 707.2 in first, 562.4 in second and 559.2 in third cUS scans, respectively). cUS = cranial ultrasound; PVE = periventricular echogenicity; PVL = periventricular leukomalacia.
and normal PVE groups. Variance exhibited opposite patterns in the two groups: it increased in the PVL group and decreased in the normal PVE group. R21 of VMR had the highest accuracy with an AUC of 0.820 for early differential diagnosis of PVL from normal PVE and had a positive predictive value of 71.5% and negative predictive value of 100% by the relative optimal cutoff value. Thus, the use of TA in serial cUS scans allows for the early prediction of PVL. Cranial US is widely used as a screening option for PVL diagnosis in preterm infants. However, the diagnostic value of cUS has been reported to vary widely, with reports of sensitivity ranging from 18%96% and those of specificity ranging from 69%99% (Franckx et al. 2018). This is due to the limitations of US, which is inherently subjective, and the limitations of assessing variable brain tissue changes at any one time. TA of serial cUS can overcome these limitations. Specifically, TA of serial cUS scans quantified the change in PVE and
revealed relatively high sensitivity and specificity in the discrimination of PVL from normal PVE. Narchi et al. (2013) performed TA of initial cUS scans obtained during the first week of life to discriminate PVL from normal PVE using MaZda software. These authors used parameters that revealed a significant difference between the two groups; however, in the present study, there was no significant difference in the texture parameters of the first cUS scan between the PVL and normal PVE groups. This discrepancy may result from differences in lesion location and in the method of drawing ROIs between the studies. Although we drew the ROI covering the entire PVE in both hemispheres on the coronal view, Narchi et al. placed the ROI as a square within the PVE in one hemisphere on each coronal and sagittal view. In addition, normalization of texture parameters was not performed. Therefore, the texture parameters of this previous study could be more susceptible to bias.
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Fig. 5. Receiver operating characteristic curves for relative optimal cutoff values of R21 of VMR, Perc. 99%, and MaxNorm for early discrimination of PVL from normal PVE. The R21 of the VMR had the highest AUC value of 0.820 (95% confidence interval: 0.5871.000; sensitivity: 100%; specificity: 60%; cutoff value: 0.842; p = 0.016). AUC = area under the receiver operating characteristic curve; PVE = periventricular echogenicity; PVL = periventricular leukomalacia; R21 = ratio of second to first cranial ultrasound scans; VMR = variance-to-mean ratio.
Histograms are obtained via the intensity of pixels, without consideration of any spatial relations between the pixels within the image (Szczypinski et al. 2009). Variance is an image feature based on a first-order histogram, and it is the difference in luminance that makes an object or its representation distinguishable in an image or an ROI display. Namely, variance is a histogram parameter that can be used to determine contrast volatility in an image (Gentillon et al. 2016). In our study, variance increased in the PVL group and decreased in the normal PVE group during the first and second cUS scans. In other words, participants with PVL tended to exhibit an increase in the heterogeneity of PVE pixel intensity, which can be connected to the coarseness of PVE as a qualitative assessment in daily practice. In ROC analysis, R21 of VMR had the highest AUC value among the texture parameters of the second cUS scan and values of R21. This result is somewhat consistent with the change in variance seen between the first and second cUS scans exhibiting a tendency to be reversed in both groups. The larger the difference between the coefficients of dispersion (i.e., VMR values), the greater was the variability between ROIs (Gentillon et al. 2016). Normal PVE is fine and regular, like brush strokes, and could be related to the orientation of normal fiber tracts being perpendicular to the ultrasonic beam, which indicates the normal anisotropy (DiPietro et al. 1986).
On the other hand, PVL was diagnosed when white matter cysts were identified; however, the process of PVL commences within a few hours after insult (Rezaie and Dean 2002), and variable changes occur, such as white matter necrosis with axonal swellings and mineralization, as well as lesions characterized by lipid-laden macrophages, activated microglia, reactive astrocytes and small cavities or cysts (Dambska et al. 1989; Deguchi et al. 1997, 1999; Hirayama et al. 2001). These microscopic changes lead to decreased anisotropy of normal fiber tracts, which reveal heterogeneous and coarse echogenicity, and would manifest as an increase in the variance of PVE during the first and second cUS scans. There are, however, limitations in using TA in routine clinical settings. The clinical applicability of TA depends on the reproducibility of the methods, but it has not been widely evaluated. The interobserver agreement has been reported, with intraclass correlation coefficients of 0.590.98 in some studies using MaZda software, as in our study, and correlation coefficients of 0.750.98 for some significant parameters (MacKay et al. 2018; Mannil et al. 2017; Yi et al. 2018). Also, the intraobserver intraclass correlation coefficients ranged from 0.790.95 (Baessler et al. 2018; Yi et al. 2018). These results suggest the fair reproducibility of TA using the MaZda method. Another limitation is the complexity of the process of TA, which consists of imaging acquisition, segmentation,
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post-processing and reporting; this process is quite labor intensive and time consuming. As the study of TA is ongoing, it may be possible to standardize these procedures and integrate this technique into US systems. TA would be widely used as a way to overcome the intrinsic subjectivity of US. In this respect, our study could help to predict PVL more easily and intuitively by observing changes in texture parameter values of serial cUS scans. Notably, there are several limitations to our study. First, this study was performed retrospectively and therefore is susceptible to inherent selection bias. Second, the sample of participants was relatively small. However, our study numbers compare favorably with a previous study that suggested that inclusion of 10 or more infants in each group with PVL or PVE offered adequate statistical power (Narchi et al. 2013). A third limitation is that we diagnosed PVL using cUS as well as MRI after termequivalent age because follow-up MRI was not obtained in the majority of participants with normal PVE. However, MRI is the gold standard for PVL diagnosis, and cUS is not as sensitive because some cases of PVL do not progress to cavities or cysts, which are apparent on cUS. Therefore, the normal PVE group might contain false-negative PVLs. Fourth, we did not consider PVL severity. However, in the present study, the incidence of cerebral palsy in the PVL group was 75%, which is similar to the 78% incidence of cerebral palsy reported in a systematic review (Hielkema and Hadders-Algra 2016). In other words, our research participants were not significantly different from other populations in terms of disease severity. Fifth, we evaluated the texture features of cUS without respect to scanner settings. The absolute pixel intensities in US can differ considerably, depending on the patient, operator and machine involved. However, we analyzed serial cUS scans of the same participants using the same US machine examined by one operator and also conducted normalization of the texture features to minimize these variations. Lastly, a single user performed the segmentation and workflow using a TA program from a single vendor in the present study. Also we did not evaluate intraobserver variability or interobserver variability. Therefore, the results may not be generalizable to other users or vendors’ methods of TA. Further research is needed to establish standardized parameters for TA and to validate the performance of the predictive parameters. CONCLUSIONS Early prediction of PVL in preterm infants is important for proper rehabilitation. Cranial US is widely used for PVL diagnosis; however, it has the inherent limitation of difficulty providing objective results. Therefore, as quantitative US is being considered as a means to
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overcome this problem, we performed quantitative TA of serial cUS scans in infants born very preterm whose first cUS scan revealed PVE. Texture parameters exhibited significant differences during the first and second cUS scans between the PVL and normal PVE groups, as well as between the first and second cUS scans in each of the two groups, although there was no significant difference in texture parameters of the first cUS scan between the two groups. Quantitative TA of serial cUS in infants born very preterm can predict PVL within the first 23 wk of life. SUPPLEMENTARY DATA Supplementary data related to this article can be found online at doi:10.1016/j.ultrasmedbio.2019.06.413. REFERENCES Baessler B, Mannil M, Oebel S, Maintz D, Alkadhi H, Manka R. Subacute and chronic left ventricular myocardial scar: Accuracy of texture analysis on nonenhanced cine MR images. Radiology 2018;286:103–112. Beller T, Peylan T, Ben Sira L, Shiran SI, Levi L, Bassan H. Quantitative analysis of cranial ultrasonographic periventricular echogenicity in relation to early neuromotor development in preterm infants. Arch Dis Child Fetal Neonatal Ed 2016;101:F217–F222. Dambska M, Laure-Kamionowska M, Schmidt-Sidor B. Early and late neuropathological changes in perinatal white matter damage. J Child Neurol 1989;4:291–298. Deguchi K, Oguchi K, Matsuura N, Armstrong DD, Takashima S. Periventricular leukomalacia: Relation to gestational age and axonal injury. Pediatr Neurol 1999;20:370–374. Deguchi K, Oguchi K, Takashima S. Characteristic neuropathology of leukomalacia in extremely low birth weight infants. Pediatr Neurol 1997;16:296–300. DiPietro MA, Brody BA, Teele RL. Peritrigonal echogenic "blush" on cranial sonography: Pathologic correlates. AJR Am J Roentgenol 1986;146:1067–1072. Franckx H, Hasaerts D, Huysentruyt K, Cools F. Cranial ultrasound and neurophysiological testing to predict neurological outcome in infants born very preterm. Dev Med Child Neurol 2018;60:1232–1238. Gentillon H, Stefanczyk L, Strzelecki M, Respondek-Liberska M. Parameter set for computer-assisted texture analysis of fetal brain. BMC Res Notes 2016;9:496. Hielkema T, Hadders-Algra M. Motor and cognitive outcome after specific early lesions of the brain—A systematic review. Dev Med Child Neurol 2016;58(Suppl. 4) 4645. Hirayama A, Okoshi Y, Hachiya Y, Ozawa Y, Ito M, Kida Y, Imai Y, Kohsaka S, Takashima S. Early immunohistochemical detection of axonal damage and glial activation in extremely immature brains with periventricular leukomalacia. Clin Neuropathol 2001;20:87–91. Inder TE, Anderson NJ, Spencer C, Wells S, Volpe JJ. White matter injury in the premature infant: A comparison between serial cranial sonographic and MR findings at term. AJNR Am J Neuroradiol 2003;24:805–809. Khwaja O, Volpe JJ. Pathogenesis of cerebral white matter injury of prematurity. Arch Dis Child Fetal Neonatal Ed 2008;93:F153–F161. Leijser LM, Srinivasan L, Rutherford MA, van Wezel-Meijler G, Counsell SJ, Allsop JM, Cowan FM. Frequently encountered cranial ultrasound features in the white matter of preterm infants: Correlation with MRI. Eur J Paediatr Neurol 2009;13:317–326. Linsell L, Malouf R, Morris J, Kurinczuk JJ, Marlow N. Prognostic factors for cerebral palsy and motor impairment in children born very preterm or very low birthweight: A systematic review. Dev Med Child Neurol 2016;58:554–569.
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https://doi.org/10.1016/j.ultrasmedbio.2019.06.413
Original Contribution EARLY PREDICTION OF PERIVENTRICULAR LEUKOMALACIA USING QUANTITATIVE TEXTURE ANALYSIS OF SERIAL CRANIAL ULTRASOUND SCANS IN VERY PRETERM INFANTS AGEDPH T YE
NA JUNG,* SANG-IL SUH,* ARIM PARK,* GUN-HA KIM,y and INSEON RYOO*TAGEDEN
* Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea; and y Department of Pediatrics, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea (Received 13 March 2019; revised 21 May 2019; in final from 21 June 2019)
Abstract—We compared texture parameters of serial cranial ultrasound (cUS) images of periventricular leukomalacia (PVL) and normal periventricular echogenicity (PVE) in very preterm infants and evaluated the early predictive values of texture analysis (TA) for PVL. Ten individuals with PVL and 10 control individuals with PVE assessed with an initial cUS within 1 wk of birth and follow-up cUS at 23 and 46 wk of life were included. TA was performed on the region of interest of PVE at the parieto-occipital area on serial cUS. Opposite changes in variance were obtained between the first two cUS sessions in both groups (p = 0.017 in PVL and p = 0.005 in PVE). The variance-to-mean ratio (VMR) between the second and first cUS sessions differed (p = 0.016) and reliably stratified the groups (area under the receiver operating characteristic curve: 0.820, 95% confidence interval: 0.5871.000, sensitivity: 100%, specificity: 60%). TA of serial cUS helps to predict PVL within 3 wk of life. (E-mails: [email protected], [email protected]) © 2019 World Federation for Ultrasound in Medicine & Biology. All rights reserved. Key Words: Index of dispersion, Periventricular leukomalacia, Preterm infant, Quantitative texture analysis, Serial cranial ultrasound, Variance.
the identification of periventricular echogenicity (PVE), the prestage of PVL, is limited partially because of its intrinsic subjectivity influenced by differences in ultrasound (US) machines, acquisition settings and variability in the experience of examiners (Tenorio et al. 2014). Furthermore, PVE is often transient and resolves spontaneously without an increased risk of neurodevelopmental disabilities, though it sometimes evolves several weeks later into leukomalacia (Narchi et al. 2013). Prior studies involving both cUS and magnetic resonance imaging (MRI) suggested that PVE may be a mild form of white matter injury (Inder et al. 2003; Maalouf et al. 2001; Miller et al. 2003), that is, the non-cystic PVL variant (Khwaja and Volpe 2008). On the other hand, some reports have hypothesized that PVE could reflect normal or delayed physiological maturational processes (Leijser et al. 2009; Vansteenkiste et al. 2009). Much effort has been extended to develop a system for the quantitative analysis of cUS scans of preterm infants to overcome the limitations of US, that is, the problems of intrinsic subjectivity (Beller et al. 2016; Narchi et al. 2013; Padilla et al. 2009; Simaeys et al. 2000; Tenorio et al. 2011, 2014; Yoshizawa et al. 2009; You et al. 2015). Several studies have reported on the
INTRODUCTION The prevalence of premature birth has gradually increased, and it is known that very preterm infants (born before 33 wk of gestation) are at high risk of adverse neurodevelopmental disability (Platt et al. 2007). Periventricular leukomalacia (PVL) is a wellknown form of periventricular white matter injury, and is a characteristic pattern of brain injury in preterm infants that has been long established as a predictor of cerebral palsy (Linsell et al. 2016). A systemic review reported that the median prevalence rate of cerebral palsy after cystic PVL was 86% (Hielkema and Hadders-Algra 2016). Therefore, early prediction of PVL is important for counseling parents and for ensuring highrisk infants receive appropriate rehabilitation (Vansteenkiste et al. 2009). Periventricular leukomalacia, especially cystic PVL, is easily detectable using cranial ultrasound (cUS). However, Address correspondence to: Sang-il Suh, Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148 Guro-dong, Guro-gu, Seoul 08308, Korea. E-mails: [email protected], [email protected]
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usefulness of cUS texture analysis (TA) (Narchi et al. 2013; Tenorio et al. 2011, 2014; You et al. 2015). TA is an automated computerized method that provides an objective, quantitative assessment of lesion heterogeneity by analyzing the distribution and relationship between pixel or voxel gray levels in the image (Narchi et al. 2013). However, no published study has evaluated and compared texture features on serial cUS scans. PVL goes through changes distinct from those of normal PVE, whether PVE is the manifestation of a repair process for mild white matter injury or physiological maturation. We hypothesized that PVL would exhibit different patterns in the TA of serial cUS compared with normal PVE. Therefore, the goals of the present study were to compare PVE patterns in very preterm infants with PVL and normal PVE using quantitative TA of cUS and evaluate the feasibility of TA of serial cUS for the early prediction of PVL. METHODS The institutional review board approved this retrospective study. The requirement for patient consent for use of clinical data was waived. Participants We searched the medical database of our institution to identify infants who underwent cUS scans between April 2011 and June 2016. We included 127 participants born between 25 and 33 wk of gestation who underwent
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an initial cUS scan within 1 wk of birth and then followup cUS scans at 23 wk (second cUS) and 46 wk (third cUS) of life. Ninety-one participants with PVE on initial cUS scan were included in our study. PVE was defined as a confluent area of increased PVE at the parieto-occipital area that was bright or brighter than the choroid plexus on coronal cUS views. Seventy-one participants were excluded because of poor image quality (n = 32), sepsis or central nervous system infection (n = 21), major cerebral pathologies such as intraventricular hemorrhage grade III or IV (n = 13) and cerebral infarction (n = 1) and use of a different US machine during the follow-up period (n = 4). Finally, 20 participants were included in the study who were diagnosed with PVL (n = 10; five males and five females) and normal PVE (n = 10; eight males and two females) on the basis of follow-up brain MRI or cUS after the term-equivalent age (Fig. 1). One neuroradiologist with 17 y of experience with brain imaging, who was blinded to the clinical findings, visually assessed those images and diagnosed PVL when cavitation or cyst formation was observed in the periventricular white matter. Image acquisition All scans were performed by a neuroradiologist with 17 y of cUS experience using an iU22 US scanner (Philips Medical Systems, Bothell, WA, USA) with a sector probe (58 Hz). The operator adjusted the machine settings, including scan gain, depth and timegain compensation, to optimize the image settings for
Fig. 1. Flowchart of study groups and participants. cUS = cranial ultrasound; MRI = magnetic resonance imaging; PVE = periventricular echogenicity; PVL = periventricular leukomalacia.
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each examination. Routine images were obtained in the coronal, sagittal and parasagittal planes through the anterior fontanel. We selected coronal images that were posterior to the ventricular antrum, which contains the white matter over the occipital horn of the lateral ventricles, and downloaded them in the original Digital Imaging and Communications in Medicine format. Texture analysis of cUS For TA, regions of interest (ROIs) were manually drawn by a neuroradiologist with 4 y of experience with cUS using the freeware MaZda Version 4.5 (Institute of Electronics, Technical University of Lodz, Lodz, Poland); the neuroradiologist was blinded to clinical results. The largest possible polygonal ROIs within the PVE were placed in both hemispheres on the downloaded coronal images with care so as to minimize the partial volume averaging effect (Fig. 2). Next, TA was performed for each ROI using the MaZda program, and we conducted normalization by remapping an image histogram within three standard deviations below and above the mean to reduce the dependency of higher-order parameters on first-order gray-level distribution and to correct technical intra- and interscanner fluctuations, as was done previously in similar investigations (Mannil et al. 2017; Meyer et al. 2017; Orphanidou-Vlachou et al. 2014; Szczypinski et al. 2009). Texture analysis yielded 308 features and we added an index of dispersion known as the variance-to-mean ratio (VMR). This measurement was used to determine the
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clustering or dispersion of luminance and to quantify the volatility (difference) in each ROI (Gentillon et al. 2016). Statistical analyses Comparisons of clinical data between the PVL and normal PVE groups were made using Fisher’s exact test for categorical variables and the independent t-test or MannWhitney U-test for continuous variables. We compared the differences in texture parameters for each period in each group using the Wilcoxon signed-rank test. Then, a comparative analysis was carried out between the two groups using the MannWhitney U-test. We conducted receiver operating characteristic (ROC) curve analysis. All parametric data are expressed in the format of mean (standard deviation). A p value of 0.05 was considered to indicate statistical significance. Statistical analyses were executed using SPSS Version 21.0 (IBM Corp., Armonk, NY, USA). RESULTS Characteristics of participants We evaluated clinical data including sex, gestational age, birth weight, Apgar score at 1 and 5 min, respiratory distress syndrome, ventilation care, and the times of the first, second and third cUS scans. Characteristics of the two groups are summarized in Table 1 and there were no significant differences the groups (all p values >0.05). Sixteen participants were evaluated during a relatively long-term follow-up period; mean = 35.0 mo (range: 2161 mo) for 8 participants with PVL and 36.0 mo (range: 2164 mo) for 8 participants with normal PVE. Cerebral palsy was confirmed in 6 participants (6/8, 75%) with PVL.
Table 1. Clinical characteristics of participants with PVL and normal PVE
Fig. 2. Delineation of regions of interest for quantitative texture analysis of periventricular echogenicity in both hemispheres on coronal cranial ultrasound image.
Characteristic
PVL (n = 10)
Normal PVE (n = 10)
p Value
Sex (male:female) Gestational age Birth weight (g) Apgar score 1 min 5 min RDS (n) Ventilation care (n) Cranial ultrasound (d) First Second Third
5:5 30 wk + 2 d (2.6) 1416 (434.7)
8:2 29 wk + 3 d (2.6) 1446 (425.8)
0.35 0.52 0.88
4.3 (1.95) 6.9 (2.33) 8 9
5.2 (1.40) 7.5 (1.4) 6 8
0.25 0.49 0.34 0.54
4.6 (2.59) 15.7 (3.40) 35.0 (5.77)
3.7 (2.63) 18.6 (3.47) 35.2 (4.69)
0.45 0.75 0.93
PVE = periventricular echogenicity; PVL = periventricular leukomalacia; RDS = respiratory distress syndrome. *Unless otherwise indicated, data are presented as the mean (standard deviation).
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Comparison of changes in the texture parameters for each period in each group We evaluated differences in all texture parameters between the first and second, second and third and first and third cUS scans for each group. Many texture parameters exhibited significant changes in each group; common parameters exhibiting significant changes in both groups are outlined in Supplementary Table S1 (online only). The variance between the first and second cUS scans exhibited opposite trends in the two groups, and the difference was statistically significant (p = 0.017 in PVL and p = 0.005 in PVE). Variance values increased between the first and second cUS scans in the PVL group, whereas values in the normal PVE group decreased in the same period (Figs. 3, 4). Comparison of texture parameters of serial cUS scans between PVL and normal PVE groups We compared texture parameters at each cUS and the ratio of change in parameters during each period between the PVL and normal PVE groups, respectively, as follows: first and second scans and ratio of second to first (R21), ratio of third to first (R31) and ratio of third to second (R32) cUS scans. Many parameters differed significantly between the two groups in terms of the second scan, R21, and R31 (all p values <0.05). Among them, texture parameters of the second cUS and R21 are summarized in Supplementary Table S2 (online only). R21 and R31 for both variance and VMR differed significantly between the two groups (all p values <0.016). However, there were no significant differences between the two groups in texture parameters for the first scan and the ratio of third to second cUS scans. ROC analysis for early prediction of PVL We performed ROC analysis to compare the areas under the ROC curves (AUCs) for texture parameters in the second cUS scan and values for R21 that differed significantly between the PVL and normal PVE groups. We did not consider values of R31 because the aim of this study was the early prediction of PVL, and the use of parameters from the third cUS scan did not fit this purpose. R21 of VMR, Perc. 99% and MaxNorm were significant parameters in ROC analysis (Fig. 5), with R21 of VMR being the most accurate diagnostic parameter for the discrimination of PVL from normal PVE (AUC = 0.820, 95% confidence interval [CI]: 0.587 1.000, p = 0.016) compared with other parameters (AUC = 0.770, 95% CI: 0.5500.990, p = 0.041 for Perc. 99%, and AUC = 0.760; 95% CI: 0.5390.981, p = 0.049 for MaxNorm). There was no significant parameter associated with the second cUS scan in ROC analysis.
Fig. 3. Plotting of variance values for serial cranial ultrasound scans in all participants with periventricular leukomalacia (a) and normal periventricular echogenicity (b). Values of variance between the first and second cranial ultrasound scans increased in the periventricular leukomalacia group (a) and decreased in the normal periventricular echogenicity group during same period. The ratio of second to first cranial ultrasound scans in terms of variance significantly differed between the two groups (p < 0.007). *The black dotted line is the trend line for the average value in each group, respectively.
We established a cutoff value of 0.842 to maximize sensitivity of the R21 of VMR, with a sensitivity of 100% and a specificity of 60%. Positive and negative predictive values were calculated by binarizing R21 of VMR with a cutoff value of 0.842. R21 of VMR had a positive predictive value of 71.5% and a negative predictive value of 100%. DISCUSSION To the best of our knowledge, this study is the first to evaluate quantitatively serial cUS scans of infants born very preterm for the purpose of early PVL prediction. Changes in variance between the first and second cUS scans were statistically significant in both the PVL
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Fig. 4. Representative cases of PVL (a) and of normal PVE (b). (a) PVL: On a coronal view of the second cUS scan (aii), the area of PVE is slightly larger, the echo range is wider and the coarseness is increased compared with the first cUS scan (ai). Also, texture analysis revealed that PVE variance increased between the first and second cUS scans (value of variance: 1048.5 in first; 2010.5 in second; and 1969.9 in third cUS scans, respectively). The third cUS scan (aiii) revealed cystic evolution of PVE, and oblique coronal T2-weighted imaging at 5 mo of age (aiv) revealed undulating ventricular borders secondary to periventricular white matter volume loss. (b) Normal PVE: The second cUS (bii) is marked by the increased internal echogenicity of periventricular white matter; however, the coarseness of the echogenicity was decreased compared with the first cUS scan (bi). Texture analysis reveals declining variance between the first and second cUS scans and the first and third cUS scans (biii) (value of variance: 707.2 in first, 562.4 in second and 559.2 in third cUS scans, respectively). cUS = cranial ultrasound; PVE = periventricular echogenicity; PVL = periventricular leukomalacia.
and normal PVE groups. Variance exhibited opposite patterns in the two groups: it increased in the PVL group and decreased in the normal PVE group. R21 of VMR had the highest accuracy with an AUC of 0.820 for early differential diagnosis of PVL from normal PVE and had a positive predictive value of 71.5% and negative predictive value of 100% by the relative optimal cutoff value. Thus, the use of TA in serial cUS scans allows for the early prediction of PVL. Cranial US is widely used as a screening option for PVL diagnosis in preterm infants. However, the diagnostic value of cUS has been reported to vary widely, with reports of sensitivity ranging from 18%96% and those of specificity ranging from 69%99% (Franckx et al. 2018). This is due to the limitations of US, which is inherently subjective, and the limitations of assessing variable brain tissue changes at any one time. TA of serial cUS can overcome these limitations. Specifically, TA of serial cUS scans quantified the change in PVE and
revealed relatively high sensitivity and specificity in the discrimination of PVL from normal PVE. Narchi et al. (2013) performed TA of initial cUS scans obtained during the first week of life to discriminate PVL from normal PVE using MaZda software. These authors used parameters that revealed a significant difference between the two groups; however, in the present study, there was no significant difference in the texture parameters of the first cUS scan between the PVL and normal PVE groups. This discrepancy may result from differences in lesion location and in the method of drawing ROIs between the studies. Although we drew the ROI covering the entire PVE in both hemispheres on the coronal view, Narchi et al. placed the ROI as a square within the PVE in one hemisphere on each coronal and sagittal view. In addition, normalization of texture parameters was not performed. Therefore, the texture parameters of this previous study could be more susceptible to bias.
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Fig. 5. Receiver operating characteristic curves for relative optimal cutoff values of R21 of VMR, Perc. 99%, and MaxNorm for early discrimination of PVL from normal PVE. The R21 of the VMR had the highest AUC value of 0.820 (95% confidence interval: 0.5871.000; sensitivity: 100%; specificity: 60%; cutoff value: 0.842; p = 0.016). AUC = area under the receiver operating characteristic curve; PVE = periventricular echogenicity; PVL = periventricular leukomalacia; R21 = ratio of second to first cranial ultrasound scans; VMR = variance-to-mean ratio.
Histograms are obtained via the intensity of pixels, without consideration of any spatial relations between the pixels within the image (Szczypinski et al. 2009). Variance is an image feature based on a first-order histogram, and it is the difference in luminance that makes an object or its representation distinguishable in an image or an ROI display. Namely, variance is a histogram parameter that can be used to determine contrast volatility in an image (Gentillon et al. 2016). In our study, variance increased in the PVL group and decreased in the normal PVE group during the first and second cUS scans. In other words, participants with PVL tended to exhibit an increase in the heterogeneity of PVE pixel intensity, which can be connected to the coarseness of PVE as a qualitative assessment in daily practice. In ROC analysis, R21 of VMR had the highest AUC value among the texture parameters of the second cUS scan and values of R21. This result is somewhat consistent with the change in variance seen between the first and second cUS scans exhibiting a tendency to be reversed in both groups. The larger the difference between the coefficients of dispersion (i.e., VMR values), the greater was the variability between ROIs (Gentillon et al. 2016). Normal PVE is fine and regular, like brush strokes, and could be related to the orientation of normal fiber tracts being perpendicular to the ultrasonic beam, which indicates the normal anisotropy (DiPietro et al. 1986).
On the other hand, PVL was diagnosed when white matter cysts were identified; however, the process of PVL commences within a few hours after insult (Rezaie and Dean 2002), and variable changes occur, such as white matter necrosis with axonal swellings and mineralization, as well as lesions characterized by lipid-laden macrophages, activated microglia, reactive astrocytes and small cavities or cysts (Dambska et al. 1989; Deguchi et al. 1997, 1999; Hirayama et al. 2001). These microscopic changes lead to decreased anisotropy of normal fiber tracts, which reveal heterogeneous and coarse echogenicity, and would manifest as an increase in the variance of PVE during the first and second cUS scans. There are, however, limitations in using TA in routine clinical settings. The clinical applicability of TA depends on the reproducibility of the methods, but it has not been widely evaluated. The interobserver agreement has been reported, with intraclass correlation coefficients of 0.590.98 in some studies using MaZda software, as in our study, and correlation coefficients of 0.750.98 for some significant parameters (MacKay et al. 2018; Mannil et al. 2017; Yi et al. 2018). Also, the intraobserver intraclass correlation coefficients ranged from 0.790.95 (Baessler et al. 2018; Yi et al. 2018). These results suggest the fair reproducibility of TA using the MaZda method. Another limitation is the complexity of the process of TA, which consists of imaging acquisition, segmentation,
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post-processing and reporting; this process is quite labor intensive and time consuming. As the study of TA is ongoing, it may be possible to standardize these procedures and integrate this technique into US systems. TA would be widely used as a way to overcome the intrinsic subjectivity of US. In this respect, our study could help to predict PVL more easily and intuitively by observing changes in texture parameter values of serial cUS scans. Notably, there are several limitations to our study. First, this study was performed retrospectively and therefore is susceptible to inherent selection bias. Second, the sample of participants was relatively small. However, our study numbers compare favorably with a previous study that suggested that inclusion of 10 or more infants in each group with PVL or PVE offered adequate statistical power (Narchi et al. 2013). A third limitation is that we diagnosed PVL using cUS as well as MRI after termequivalent age because follow-up MRI was not obtained in the majority of participants with normal PVE. However, MRI is the gold standard for PVL diagnosis, and cUS is not as sensitive because some cases of PVL do not progress to cavities or cysts, which are apparent on cUS. Therefore, the normal PVE group might contain false-negative PVLs. Fourth, we did not consider PVL severity. However, in the present study, the incidence of cerebral palsy in the PVL group was 75%, which is similar to the 78% incidence of cerebral palsy reported in a systematic review (Hielkema and Hadders-Algra 2016). In other words, our research participants were not significantly different from other populations in terms of disease severity. Fifth, we evaluated the texture features of cUS without respect to scanner settings. The absolute pixel intensities in US can differ considerably, depending on the patient, operator and machine involved. However, we analyzed serial cUS scans of the same participants using the same US machine examined by one operator and also conducted normalization of the texture features to minimize these variations. Lastly, a single user performed the segmentation and workflow using a TA program from a single vendor in the present study. Also we did not evaluate intraobserver variability or interobserver variability. Therefore, the results may not be generalizable to other users or vendors’ methods of TA. Further research is needed to establish standardized parameters for TA and to validate the performance of the predictive parameters. CONCLUSIONS Early prediction of PVL in preterm infants is important for proper rehabilitation. Cranial US is widely used for PVL diagnosis; however, it has the inherent limitation of difficulty providing objective results. Therefore, as quantitative US is being considered as a means to
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overcome this problem, we performed quantitative TA of serial cUS scans in infants born very preterm whose first cUS scan revealed PVE. Texture parameters exhibited significant differences during the first and second cUS scans between the PVL and normal PVE groups, as well as between the first and second cUS scans in each of the two groups, although there was no significant difference in texture parameters of the first cUS scan between the two groups. Quantitative TA of serial cUS in infants born very preterm can predict PVL within the first 23 wk of life. SUPPLEMENTARY DATA Supplementary data related to this article can be found online at doi:10.1016/j.ultrasmedbio.2019.06.413. REFERENCES Baessler B, Mannil M, Oebel S, Maintz D, Alkadhi H, Manka R. Subacute and chronic left ventricular myocardial scar: Accuracy of texture analysis on nonenhanced cine MR images. Radiology 2018;286:103–112. Beller T, Peylan T, Ben Sira L, Shiran SI, Levi L, Bassan H. Quantitative analysis of cranial ultrasonographic periventricular echogenicity in relation to early neuromotor development in preterm infants. Arch Dis Child Fetal Neonatal Ed 2016;101:F217–F222. Dambska M, Laure-Kamionowska M, Schmidt-Sidor B. Early and late neuropathological changes in perinatal white matter damage. J Child Neurol 1989;4:291–298. Deguchi K, Oguchi K, Matsuura N, Armstrong DD, Takashima S. Periventricular leukomalacia: Relation to gestational age and axonal injury. Pediatr Neurol 1999;20:370–374. Deguchi K, Oguchi K, Takashima S. Characteristic neuropathology of leukomalacia in extremely low birth weight infants. Pediatr Neurol 1997;16:296–300. DiPietro MA, Brody BA, Teele RL. Peritrigonal echogenic "blush" on cranial sonography: Pathologic correlates. AJR Am J Roentgenol 1986;146:1067–1072. Franckx H, Hasaerts D, Huysentruyt K, Cools F. Cranial ultrasound and neurophysiological testing to predict neurological outcome in infants born very preterm. Dev Med Child Neurol 2018;60:1232–1238. Gentillon H, Stefanczyk L, Strzelecki M, Respondek-Liberska M. Parameter set for computer-assisted texture analysis of fetal brain. BMC Res Notes 2016;9:496. Hielkema T, Hadders-Algra M. Motor and cognitive outcome after specific early lesions of the brain—A systematic review. Dev Med Child Neurol 2016;58(Suppl. 4) 4645. Hirayama A, Okoshi Y, Hachiya Y, Ozawa Y, Ito M, Kida Y, Imai Y, Kohsaka S, Takashima S. Early immunohistochemical detection of axonal damage and glial activation in extremely immature brains with periventricular leukomalacia. Clin Neuropathol 2001;20:87–91. Inder TE, Anderson NJ, Spencer C, Wells S, Volpe JJ. White matter injury in the premature infant: A comparison between serial cranial sonographic and MR findings at term. AJNR Am J Neuroradiol 2003;24:805–809. Khwaja O, Volpe JJ. Pathogenesis of cerebral white matter injury of prematurity. Arch Dis Child Fetal Neonatal Ed 2008;93:F153–F161. Leijser LM, Srinivasan L, Rutherford MA, van Wezel-Meijler G, Counsell SJ, Allsop JM, Cowan FM. Frequently encountered cranial ultrasound features in the white matter of preterm infants: Correlation with MRI. Eur J Paediatr Neurol 2009;13:317–326. Linsell L, Malouf R, Morris J, Kurinczuk JJ, Marlow N. Prognostic factors for cerebral palsy and motor impairment in children born very preterm or very low birthweight: A systematic review. Dev Med Child Neurol 2016;58:554–569.
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