A retrospective study for the computed tomography radiomics changes of bone and muscles in patients with chronic obstructive pulmonary disease

To investigate the computed tomography (CT) radiomics changes of bone and muscles in patients with chronic obstructive pulmonary disease (COPD). 102 COPD patients and 102 healthy controls from center A, and 96 COPD patients and 136 healthy controls from center B were included in the study. By using ‘TotalSegmentator’ of 3D slicer to process chest CT data, multiple organs were automatically segmented, including: spleen, liver, Th1-11 vertebra, humerus, scapula, spinal cord, deep back muscle and sternum. 1953 radiomics features were extracted from these organs. The dimensionality reduction of features were maximum relevancy and minimum redundancy (mRMR) and Least Absolute Selection and Shrinkage Operator (LASSO). The Receiver Operating Characteristic (ROC) curve was used for determine performance in identification of COPD. Area under curve (AUC) and accuracy were calculated for radiomics score and features. 190 CT features were significantly different between COPD and healthy cases. The AUC of radiomics score of Th11, Th10, left scapula and left deep back muscle were 0.791, 0.755, 0.722 and 0.714 respectively. By using mRMR, 30 features were selected from 190 features. By using LASSO, eighteen features had non-zero coefficients. In the 18 features, Th11, Th10, left scapula and left deep back muscle had more than one features. In the external data, the radiomics score of multiple organs had AUC of 0.863(0.814,0.907), sensitivity of 0.719 (0.623, 0.811), specificity of 0.897(0.841, 0.945), and accuracy of 0.816 (0.766, 0.867). CT radiomics is feasible in evaluating bone and muscles changes of patients with COPD. Th11, Th10, scapula and deep back muscle are more likely influenced by COPD.

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Publication Details

Journal
BMC Pulmonary Medicine
Published
2026-11-02
DOI
https://doi.org/10.1186/s12890-026-04890-z
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

A retrospective study for the computed tomography radiomics changes of bone and muscles in patients with chronic obstructive pulmonary disease

Ruimin Guo, Gang Wu, Hongwei Ren, Nadeer M Gharaibeh et al.
2 links
BMC Pulmonary Medicine
Radiomics and Machine Learning in Medical Imaging
article

A retrospective study for the computed tomography radiomics changes of bone and muscles in patients with chronic obstructive pulmonary disease

Ruimin Guo, Gang Wu, Hongwei Ren, Nadeer M Gharaibeh, Ting Li
article en

Abstract

To investigate the computed tomography (CT) radiomics changes of bone and muscles in patients with chronic obstructive pulmonary disease (COPD). 102 COPD patients and 102 healthy controls from center A, and 96 COPD patients and 136 healthy controls from center B were included in the study. By using ‘TotalSegmentator’ of 3D slicer to process chest CT data, multiple organs were automatically segmented, including: spleen, liver, Th1-11 vertebra, humerus, scapula, spinal cord, deep back muscle and sternum. 1953 radiomics features were extracted from these organs. The dimensionality reduction of features were maximum relevancy and minimum redundancy (mRMR) and Least Absolute Selection and Shrinkage Operator (LASSO). The Receiver Operating Characteristic (ROC) curve was used for determine performance in identification of COPD. Area under curve (AUC) and accuracy were calculated for radiomics score and features. 190 CT features were significantly different between COPD and healthy cases. The AUC of radiomics score of Th11, Th10, left scapula and left deep back muscle were 0.791, 0.755, 0.722 and 0.714 respectively. By using mRMR, 30 features were selected from 190 features. By using LASSO, eighteen features had non-zero coefficients. In the 18 features, Th11, Th10, left scapula and left deep back muscle had more than one features. In the external data, the radiomics score of multiple organs had AUC of 0.863(0.814,0.907), sensitivity of 0.719 (0.623, 0.811), specificity of 0.897(0.841, 0.945), and accuracy of 0.816 (0.766, 0.867). CT radiomics is feasible in evaluating bone and muscles changes of patients with COPD. Th11, Th10, scapula and deep back muscle are more likely influenced by COPD.

BMC Pulmonary Medicine
Central Hospital of Wuhan (CN), Tongji Hospital (CN), Wuhan University of Science and Technology (CN), Huazhong University of Science and Technology (CN)
National Key Research and Development Program of China
Good health and well-being
Openalex Percentile: Top 15%
Radiomics and Machine Learning in Medical Imaging
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