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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Neurodevelopment
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1386340
This article is part of the Research Topic Long-Lasting Neurobehavioral Effects of Early-Life Events View all 6 articles

Predicting Neurodevelopmental Outcomes in Extremely Preterm Neonates with Low-Grade Germinal Matrix-Intraventricular Hemorrhage Using Synthetic MRI

Provisionally accepted
Zhang Chunxiang Zhang Chunxiang 1,2,3Zitao Zhu Zitao Zhu 4*Kaiyu Wang Kaiyu Wang 5*Xiaoan Zhang Xiaoan Zhang 2Zhao Xin Zhao Xin 2,3*
  • 1 Harvard Medical School, Boston, Massachusetts, United States
  • 2 Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
  • 3 Henan International Joint Laboratory of Neuroimaging, Zhengzhou University, zhengzhou, China
  • 4 Wuhan University, Wuhan, Hubei Province, China
  • 5 MRI Research, GE Healthcare (China), Beijing, China

The final, formatted version of the article will be published soon.

    Objectives: This study aims to assess the predictive capability of synthetic MRI in assessing neurodevelopmental outcomes for extremely preterm neonates with low-grade Germinal Matrix-Intraventricular Hemorrhage (GMH-IVH). The study also investigates the potential enhancement of predictive performance by combining relaxation times from different brain regions. Materials and Methods: In this prospective study, eighty extremely preterm neonates with GMH-IVH underwent synthetic MRI around 38 weeks, between January 2020 and June 2022. Neurodevelopmental assessments at 18 months of corrected age categorized the infants into two groups: those without disability (n = 40) and those with disability (n = 40), with cognitive and motor outcome scores recorded. T1, T2 relaxation times, and Proton Density (PD) values were measured in different brain regions. Logistic regression analysis was utilized to correlate MRI values with neurodevelopmental outcome scores. Synthetic MRI metrics linked to disability were identified, and combined models with independent predictors were established. The predictability of synthetic MRI metrics in different brain regions and their combinations were evaluated and compared with internal validation using bootstrap resampling. Results: Elevated T1 and T2 relaxation times in the frontal white matter (FWM) and caudate were significantly associated with disability (p < 0.05). The T1 -FWM, T1 -Caudate, T2 -FWM, and T2 -Caudate models exhibited overall predictive performance with AUC values of 0.751, 0.695, 0.856, and 0.872, respectively. Combining these models into T1- FWM + T1 -Caudate + T2 - FWM + T2 -Caudate resulted in an improved AUC of 0.955, surpassing individual models (p < 0.05). Bootstrap resampling confirmed the validity of the models. Conclusions: Synthetic MRI proves effective in early predicting adverse outcomes in extremely preterm infants with GMH-IVH. The combination of T1- FWM + T1-Caudate + T2- FWM + T2-Caudate further enhances predictive accuracy, offering valuable insights for early intervention strategies.

    Keywords: Synthetic MRI, Germinal matrix-intraventricular hemorrhage, Extremely preterm infants, Neurodevelopmental Outcomes, Predictive modeling Abbreviations GMH-IVH Germinal Matrix-Intraventricular Hemorrhage T1 Longitudinal relaxation time T2 Transverse relaxation time PD Proton density

    Received: 15 Feb 2024; Accepted: 26 Jul 2024.

    Copyright: © 2024 Chunxiang, Zhu, Wang, Zhang and Xin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Zitao Zhu, Wuhan University, Wuhan, 430072, Hubei Province, China
    Kaiyu Wang, MRI Research, GE Healthcare (China), Beijing, China
    Zhao Xin, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.