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Multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis and neuropsychiatric systemic lupus erythematosus

Abstract

Objectives

To develop an MRI-based multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis (RRMS) and its mimicker neuropsychiatric systemic lupus erythematosus (NPSLE).

Methods

A total of 112 patients with RRMS (n = 63) or NPSLE (n = 49) were assigned to training and test sets with a ratio of 3:1. All lesions across the whole brain were manually segmented on T2-weighted fluid-attenuated inversion recovery images. For each single lesion, 371 radiomics features were extracted and trained using machine learning algorithms, producing Radiomics Index for Lesion (RIL) for each lesion and a single-lesion radiomics model. Then, for each subject, single lesions were assigned to one of two disease courts based on their distance to decision threshold, and a Radiomics Index for Subject (RIS) was calculated as the mean RIL value of lesions on the higher-weighted court. Accordingly, a subject-level discrimination model was constructed and compared with performances of two radiologists.

Results

The subject-based discrimination model satisfactorily differentiated RRMS and NPSLE in both training (AUC = 0.967, accuracy = 0.892, sensitivity = 0.917, and specificity = 0.872) and test sets (AUC = 0.962, accuracy = 0.931, sensitivity = 1.000, and specificity = 0.875), significantly better than the single-lesion radiomics method (training: p < 0.001; test: p = 0.001) Besides, the discrimination model significantly outperformed the senior radiologist in the training set (training: p = 0.018; test: p = 0.077) and the junior radiologist in both the training and test sets (training: p = 0.008; test: p = 0.023).

Conclusions

The multi-lesion radiomics model could effectively discriminate between RRMS and NPSLE, providing a supplementary tool for accurate differential diagnosis of the two diseases.

Key Points

Radiomic features of brain lesions in RRMS and NPSLE were different.

The multi-lesion radiomics model constructed using a merging strategy was comprehensively superior to the single-lesion-based model for discrimination of RRMS and NPSLE.

The RRMS-NPSLE discrimination model showed a significantly better performance or a trend toward significance than the radiologists.

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Abbreviations

CNS:

Central nervous system

MRI:

Magnetic resonance imaging

MS:

Multiple sclerosis

NPSLE:

Neuropsychiatric systemic lupus erythematosus

RIL:

Radiomics Index for Lesion

RIS:

Radiomics Index for Subject

RRMS:

Relapsing-remitting multiple sclerosis

SLE:

Systemic lupus erythematosus

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Funding

This study has received funding from the National Key R&D Program of China (2019YFC0120602), the Science and Technology Commission of Shanghai Municipality (20S31904300), Clinical Research Plan of SHDC (SHDC2020CR3020A), the National Science Foundation for Young Scholars of China (81703112 and 82102132), and the Natural Science Foundation of Shanghai (General Program) (22ZR1409500).

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Authors and Affiliations

Authors

Corresponding authors

Correspondence to Hao Wu or Liqin Yang.

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Guarantor

The scientific guarantor of this publication is Daoying Geng.

Conflict of interest

The authors declare no competing interests.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• cross-sectional study

• performed at one institution

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Cite this article

Luo, X., Piao, S., Li, H. et al. Multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis and neuropsychiatric systemic lupus erythematosus. Eur Radiol 32, 5700–5710 (2022). https://doi.org/10.1007/s00330-022-08653-2

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  • DOI: https://doi.org/10.1007/s00330-022-08653-2

Keywords

  • Multiple sclerosis, relapsing-remitting
  • Lupus erythematosus, systemic
  • Machine learning
  • Support vector machine
  • Image processing, computer-assisted