Dataset Information

Objectively Diagnosed Diffuse White Matter Abnormality at Term Is an Independent Predictor of Cognitive and Language Outcomes in Infants Born Very Preterm.

ABSTRACT:

Objective

To externally validate the independent value of objectively diagnosed diffuse white matter abnormality (DWMA; also known as diffuse excessive high signal intensity) volume to predict neurodevelopmental outcomes in very preterm infants (≤31 weeks of gestational age).

Study design

A prospective, multicenter, regional population-based cohort study in 98 very preterm infants without severe brain injury on magnetic resonance imaging (MRI). DWMA volume was diagnosed objectively on structural MRI at term-equivalent age using our published algorithm. Multivariable linear regression was used to assess the value of DWMA volume to predict cognitive and language scores on the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III) at 2 years corrected age.

Results

Of the infants who returned for follow-up (n = 74), the mean (SD) gestational age was 28.2 (2.4) weeks, and 42 (56.8%) were boys. In bivariable analyses, DWMA volume was a significant predictor of Bayley-III cognitive and language scores. In multivariable analyses, controlling for known predictors of Bayley-III scores (ie, socioeconomic status, gestational age, sex, and global brain abnormality score), DWMA volume remained a significant predictor of cognitive (P < .001) and language (P = .04) scores at 2 years. When dichotomized, objectively diagnosed severe DWMA was a significant predictor of cognitive and language impairments, whereas visual qualitative diagnosis of DWMA was a poor predictor.

Conclusions

In this multicenter, prospective cohort study, we externally validated our previous findings that objectively diagnosed DWMA is an independent predictor of cognitive and language development in very preterm infants. We also demonstrated again that visually-diagnosed DWMA is not predictive of neurodevelopmental outcomes.

SUBMITTER: Parikh NA

PROVIDER: S-EPMC7583652 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Diffuse white matter abnormality (DWMA), or diffuse excessive high signal intensity is observed in 50-80% of very preterm infants at term-equivalent age. It is subjectively defined as higher than normal signal intensity in periventricular and subcortical white matter in comparison to normal unmyelinated white matter on T2-weighted MRI images. Despite the well-documented presence of DWMA, it remains debatable whether DWMA represents pathological tissue injury or a transient developmental phenomenon. Manual tracing of DWMA exhibits poor reliability and reproducibility and unduly increases image processing time. Thus, objective and ideally automatic assessment is critical to accurately elucidate the biologic nature of DWMA. We propose a deep learning approach to automatically identify DWMA regions on T2-weighted MRI images. Specifically, we formulated DWMA detection as an image voxel classification task; that is, the voxels on T2-weighted images are treated as samples and exclusively assigned as DWMA or normal white matter voxel classes. To utilize the spatial information of individual voxels, small image patches centered on the given voxels are retrieved. A deep convolutional neural networks (CNN) model was developed to differentiate DWMA and normal voxels. We tested our deep CNN in multiple validation experiments. First, we examined DWMA detection accuracy of our CNN model using computer simulations. This was followed by in vivo assessments in a cohort of very preterm infants (N = 95) using cross-validation and holdout validation. Finally, we tested our approach on an independent preterm cohort (N = 28) to externally validate our model. Our deep CNN model achieved Dice similarity index values ranging from 0.85 to 0.99 for DWMA detection in the aforementioned validation experiments. Our proposed deep CNN model exhibited significantly better performance than other popular machine learning models. We present an objective and automated approach for accurately identifying DWMA that may facilitate the clinical diagnosis of DWMA in very preterm infants.