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Radiomics from magnetic resonance imaging may be used to predict the progression of white matter hyperintensities and identify associated risk factors

Abstract

Objective

The progression of white matter hyperintensities (WMH) varies considerably in adults. In this study, we aimed to predict the progression and related risk factors of WMH based on the radiomics of whole-brain white matter (WBWM).

Methods

A retrospective analysis was conducted on 141 patients with WMH who underwent two consecutive brain magnetic resonance (MR) imaging sessions from March 2014 to May 2018. The WBWM was segmented to extract and score the radiomics features at baseline. Follow-up images were evaluated using the modified Fazekas scale, with progression indicated by scores ≥ 1. Patients were divided into progressive (n = 65) and non-progressive (n = 76) groups. The progressive group was subdivided into any WMH (AWMH), periventricular WMH (PWMH), and deep WMH (DWMH). Independent risk factors were identified using logistic regression.

Results

The area under the curve (AUC) values for the radiomics signatures of the training sets were 0.758, 0.749, and 0.775 for AWMH, PWMH, and DWMH, respectively. The AUC values of the validation set were 0.714, 0.697, and 0.717, respectively. Age and hyperlipidemia were independent predictors of progression for AWMH. Age and body mass index (BMI) were independent predictors of progression for DWMH, while hyperlipidemia was an independent predictor of progression for PWMH. After combining clinical factors and radiomics signatures, the AUC values were 0.848, 0.863, and 0.861, respectively, for the training set, and 0.824, 0.818, and 0.833, respectively, for the validation set.

Conclusions

MRI-based radiomics of WBWM, along with specific risk factors, may allow physicians to predict the progression of WMH.

Key Points

• Radiomics features detected by magnetic resonance imaging may be used to predict the progression of white matter hyperintensities.

• Radiomics may be used to identify risk factors associated with the progression of white matter hyperintensities.

• Radiomics may serve as non-invasive biomarkers to monitor white matter status.

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Abbreviations

AUC:

Area under the curve

AWMH:

Progression of WMH in any (periventricular and/or deep) region

BMI:

Body mass index

DTI:

Diffusion tensor imaging

DWMH:

Deep white matter hyperintensities

FLAIR:

Fluid attenuated inversion recovery

GLCM:

Gray-level co-occurrence matrix

ICC:

Intraclass correlation coefficient

LASSO:

The least absolute shrinkage and selection operator

LDL:

Low-density lipoprotein

MRI:

Magnetic resonance imaging

NAWM:

Normal-appearing white matter

PWMH:

Periventricular white matter hyperintensities

RLM:

Run-length matrix

ROC:

Receiver operating characteristic

ROI:

Region of interest

WBWM:

Whole-brain white matter

WMH:

White matter hyperintensities

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Acknowledgments

We are grateful to Ms. Peipei Pang (GE Healthcare, Shanghai, China) for her technical support.

Funding

Fund of Zhejiang Traditional Chinese Medicine Science Research Projection in China (2019ZA004) and Fund of Health Commission of Zhejiang Province in China (2019KY302).

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Authors

Corresponding authors

Correspondence to Yuyun Xu or Xiangyang Gong.

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Guarantor

The scientific guarantor of this publication is Zhenyu Shu.

Conflict of interest

One of the authors of this manuscript (Peipei Pang) is an employee of GE Healthcare China. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in Shao Y, Chen Z, Ming S, et al Predicting the Development of Normal-Appearing White Matter With Radiomics in the Aging Brain: A Longitudinal Clinical Study. Front Aging Neurosci 2018;10:393.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Shu, Z., Xu, Y., Shao, Y. et al. Radiomics from magnetic resonance imaging may be used to predict the progression of white matter hyperintensities and identify associated risk factors. Eur Radiol 30, 3046–3058 (2020). https://doi.org/10.1007/s00330-020-06676-1

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  • DOI: https://doi.org/10.1007/s00330-020-06676-1

Keywords

  • White matter
  • Magnetic resonance imaging
  • Risk factors
  • Retrospective studies
  • Neuroimaging