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The value of MRI-based radiomics for evaluating early parotid gland injury in primary Sjögren’s syndrome

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Abstract

Objective

This study aimed to evaluate the value of machine learning models (ML) based on MRI radiomics in diagnosing early parotid gland injury in primary Sjögren’s syndrome (pSS).

Methods

A total of 164 patients (114 in the training cohort and 50 in the testing cohort) with pSS (n=82) or healthy controls (HC) (n=82) were enrolled. Itksnap software was used to perform two-dimensional segmentation of the bilateral parotid glands on T1-weighted (T1WI) and fat-suppressed T2-weighted imaging (fs-T2WI) images. A total of 1548 texture features of the parotid glands were extracted using radiomics software. A radiomics score (Radscore) was constructed and calculated. A t-test was used to compare the Radscore between the two groups. Finally, five machine learning models were trained and tested to identify early pSS parotid injury, and the performance of the machine learning models was evaluated by calculating the acceptance operating curve (ROC) and other parameters.

Results

The Radscores between the pSS and HC groups showed significant statistical differences (p<0.001). Among the five machine learning models, the Extra Trees Classifier (ETC) model performed high predictive efficacy in identifying early pSS parotid injury, with an AUC of 0.87 in the testing set.

Conclusion

MRI radiomics–based machine learning models can effectively diagnose early parotid gland injury in primary Sjögren’s syndrome.

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Author information

Authors and Affiliations

  1. Medical College, Yangzhou University, Yangzhou, 255000, China

    Lu Lu, Tiantian Dai, Wei Wang & Guoqing Li

  2. Department of Radiology, Medical Imaging Center, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 255000, China

    Yi Zhao, Hang Qu, Qi an Sun, Hongyi Xia & Wei Wang

  3. Department of Rheumatology and Immunology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 255000, China

    Guoqing Li

Authors
  1. Lu Lu
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  7. Wei Wang
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Contributions

WW and GQL contributed to conception and design of the study; LL, TTD, QAS, and HYX helped with the acquisition of data; WW, HQ, YZ, and LL analyzed and interpretated the retrospective clinical study and data; LL and TTD wrote sections of the manuscript. WW and GQL reviewed the paper. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Wei Wang.

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Key Points

• MRI-based Radscore distinguishes early parotid gland injury in primary Sjögren’s syndrome from healthy glands.

• Combining MRI radiomics and machine learning enhances early parotid gland injury diagnosis in primary Sjögren’s syndrome.

Lu Lu and Tiantian Dai are first authors.

Supplementary information

ESM 1:

Table 1 The individual performance of AUROC for the 24 selected radiomics (DOCX 19 kb)

ESM 2:

Supplementary materials (DOCX 380 kb)

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Lu, L., Dai, T., Zhao, Y. et al. The value of MRI-based radiomics for evaluating early parotid gland injury in primary Sjögren’s syndrome. Clin Rheumatol 43, 1675–1682 (2024). https://doi.org/10.1007/s10067-024-06935-2

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  • DOI: https://doi.org/10.1007/s10067-024-06935-2

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