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

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.

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Publication Details

Journal
International Journal of Computer Assisted Radiology and Surgery
Published
2026-08-03
DOI
https://doi.org/10.1007/s11548-026-03771-4
Primary Topic
Systemic Sclerosis and Related Diseases
Type
article
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article

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

Hiroshi Takemura, Rea Ikeda, Yuito UEDA, Mikito Suzuki et al.
International Journal of Computer Assisted Radiology and Surgery
Systemic Sclerosis and Related Diseases
article

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

Hiroshi Takemura, Rea Ikeda, Yuito UEDA, Mikito Suzuki, Masataka Kuwana, Shinnosuke Watanabe
article en

Abstract

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.

International Journal of Computer Assisted Radiology and Surgery
Tokyo University of Science (JP), Nippon Medical School (JP)
Good health and well-being
Openalex Percentile: Top 10%
Systemic Sclerosis and Related Diseases
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