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Brain MRI radiomics analysis may predict poor psychomotor outcome in preterm neonates

  • Paediatric
  • Published:
European Radiology Aims and scope Submit manuscript

An Editorial Comment to this article was published on 20 April 2021

Abstract

Objectives

This study aimed to apply a radiomics approach to predict poor psychomotor development in preterm neonates using brain MRI.

Methods

Prospectively enrolled preterm neonates underwent brain MRI near or at term-equivalent age and neurodevelopment was assessed at a corrected age of 12 months. Two radiologists visually assessed the degree of white matter injury. The radiomics analysis on white matter was performed using T1-weighted images (T1WI) and T2-weighted images (T2WI). A total of 1906 features were extracted from the images and the minimum redundancy maximum relevance algorithm was used to select features. A prediction model for the binary classification of the psychomotor developmental index was developed and eightfold cross-validation was performed. The diagnostic performance of the model was evaluated using the AUC with and without including significant clinical and DTI parameters.

Results

A total of 46 preterm neonates (median gestational age, 29 weeks; 26 males) underwent brain MRI (median corrected gestational age, 37 weeks). Thirteen of 46 (28.3%) neonates showed poor psychomotor outcomes. There was one neonate among 46 with moderate to severe white matter injury on visual assessment. For the radiomics analysis, twenty features were selected for each analysis. The AUCs of prediction models based on T1WI, T2WI, and both T1WI and T2WI were 0.925, 0.834, and 0.902. Including gestational age or DTI parameters did not improve the prediction performance of T1WI.

Conclusions

A radiomics analysis of white matter using early T1WI or T2WI could predict poor psychomotor outcomes in preterm neonates.

Key Points

• Radiomics analysis on T1-weighted images of preterm neonates showed the highest diagnostic performance (AUC, 0.925) for predicting poor psychomotor outcomes.

• In spite of 45 of 46 neonates having no significant white matter injury on visual assessment, the radiomics analysis of early brain MRI showed good diagnostic performance (sensitivity, 84.6%; specificity, 78.8%) for predicting poor psychomotor outcomes.

• Radiomics analysis on early brain MRI can help to predict poor neurodevelopmental outcomes in preterm neonates.

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Abbreviations

AUC:

Area under the curve

FA:

Fractional anisotropy

GLCM:

Gray level co-occurrence matrix

GLDM:

Gray level dependence matrix

GLRLM:

Gray level run length matrix

GLSZM:

Gray level size zone matrix

GM:

Gray matter

LoG:

Laplacian of Gaussian-filtered

MRMR:

Minimum redundancy maximum relevance

PDI:

Psychomotor development index

PLIC:

Posterior limbs of internal capsule

ROC:

Receiver operating characteristics

ROI:

Region of interest

T1WI:

T1-weighted image

T2WI:

T2-weighted image

WM:

White matter

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Acknowledgements

The authors thank Sun Mi Cho, Department of Psychiatry, Ajou University Hospital, for contributing data on neurodevelopmental outcomes.

Funding

This study has received funding from the National Research Foundation of Korea.

Author information

Authors and Affiliations

  1. Division of Mechanical and Biomedical Engineering, Ewha Womans University, Seoul, Republic of Korea

    Youwon Shin & Taehoon Shin

  2. Department of Radiology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

    Yoonho Nam & Hyun Gi Kim

  3. Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin-si, Gyeonggi-do, Republic of Korea

    Yoonho Nam & Jiseon Lim

  4. Department of Medicine, Case Western Reserve University, Cleveland, OH, USA

    Taehoon Shin

  5. Department of Radiology, Ajou University School of Medicine, Ajou University Medical Center, Suwon, Republic of Korea

    Jin Wook Choi & Hyun Gi Kim

  6. Department of Pediatrics, Ajou University School of Medicine, Ajou University Medical Center, Suwon, Republic of Korea

    Jang Hoon Lee & Da Eun Jung

Authors
  1. Youwon Shin
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Corresponding author

Correspondence to Hyun Gi Kim.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Seung Eun Jung, MD.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects have been previously reported in European Radiology titled “Texture analysis of deep medullary veins on susceptibility-weighted imaging in infants: evaluating developmental and ischemic changes.”

Methodology

• prospective

• prognostic study

• performed at one institution

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Youwon Shin and Yoonho Nam are co-first authors. This work origination is at the Department of Radiology, Ajou University School of Medicine, Ajou University Medical Center, Yeongtong-gu, Suwon, Republic of Korea.

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Shin, Y., Nam, Y., Shin, T. et al. Brain MRI radiomics analysis may predict poor psychomotor outcome in preterm neonates. Eur Radiol 31, 6147–6155 (2021). https://doi.org/10.1007/s00330-021-07836-7

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  • DOI: https://doi.org/10.1007/s00330-021-07836-7

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