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<b>Purpose:</b> <p>Esophageal involvement is a common and early manifestation of systemic sclerosis (SSc). This involvement is mainly evaluated by esophageal manometry, but this test is invasive and provides limited morphological information. This study aimed to identify CT-based radiomics features related to esophageal involvement and to evaluate whether the presence or absence of esophageal involvement can be classified with high accuracy.</p> <b>Methods:</b> <p>Chest CT images from 64 subjects were analyzed, including 30 patients with SSc and 34 normal subjects. The esophageal region was automatically segmented, and radiomics features were extracted from ellipse-fitted regions of interest on axial slices. Features showing significant differences were selected, and feature combinations were evaluated using exhaustive search. Classification was performed using XGBoost.</p> <b>Results:</b> <p>First-order statistical features at levels corresponding to the third to fifth thoracic vertebrae were significantly associated with the presence of esophageal involvement. Classification using these features showed high accuracy when five to six features were used, indicating that the presence or absence of esophageal involvement can be identified accurately even with a small number of features.</p> <b>Conclusion:</b> <p>CT-based radiomics analysis may be a useful noninvasive method for evaluating esophageal involvement. Future studies incorporating pathological correlation and surrounding tissue information may further improve clinical applicability.</p>

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Radiomics analysis and classification of esophageal involvement in systemic sclerosis using chest CT images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Yuito Ueda,
  • Shinnosuke Watanabe,
  • Rea Ikeda,
  • Mikito Suzuki,
  • Masataka Kuwana,
  • Hiroshi Takemura

摘要

Purpose:

Esophageal involvement is a common and early manifestation of systemic sclerosis (SSc). This involvement is mainly evaluated by esophageal manometry, but this test is invasive and provides limited morphological information. This study aimed to identify CT-based radiomics features related to esophageal involvement and to evaluate whether the presence or absence of esophageal involvement can be classified with high accuracy.

Methods:

Chest CT images from 64 subjects were analyzed, including 30 patients with SSc and 34 normal subjects. The esophageal region was automatically segmented, and radiomics features were extracted from ellipse-fitted regions of interest on axial slices. Features showing significant differences were selected, and feature combinations were evaluated using exhaustive search. Classification was performed using XGBoost.

Results:

First-order statistical features at levels corresponding to the third to fifth thoracic vertebrae were significantly associated with the presence of esophageal involvement. Classification using these features showed high accuracy when five to six features were used, indicating that the presence or absence of esophageal involvement can be identified accurately even with a small number of features.

Conclusion:

CT-based radiomics analysis may be a useful noninvasive method for evaluating esophageal involvement. Future studies incorporating pathological correlation and surrounding tissue information may further improve clinical applicability.