Evidence map›Paper›PMID 42658393›Full record

ArticleInsights into imaging2026

Automated 3D chest CT muscle segmentation-derived model development and explanation in discrimination of normal, preserved ratio impaired spirometry, and COPD: a multicenter study.

Yi Wang, Weihao Zhai, Qian Zhou, Xiuxiu Zhou, Yueze Li, Taohu Zhou, Xin'ang Jiang, Qianxi Jin, Yanming Ge, Peng Dong and 3 more

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Article in Insights into imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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13 authors.

Yi Wang *Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Weihao Zhai *Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Qian ZhouDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Xiuxiu ZhouDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Yueze LiDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Taohu ZhouDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Xin'ang JiangDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Qianxi JinDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Yanming GeSchool of Medical Imaging, Shandong Second Medical University, Weifang, China.
Peng DongSchool of Medical Imaging, Shandong Second Medical University, Weifang, China.
Ruoyao WangDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Shiyuan LiuDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Li FanDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China. fanli0930@163.com.

Funding

Excellent Health Sector Program of Shanghai Municipal Health Commission 20254Z0003National Natural Science Foundation of China 82430065,82171926,81871321Shanghai Oriental Talents Program Top Talent Project SHSDFYCJHBJ-FLLShanghai Rising Stars of Medical Talent Youth Development Program for Outstanding Youth Medical Talents SHWSRS 2025-71Shanghai Science and Technology Project 21DZ2202600
6 · The paper itself

Abstract

objectivesTo develop and validate an automated 3D chest CT muscle segmentation model using an exploratory normalization method to discriminate normal, preserved ratio impaired spirometry (PRISm), and chronic obstructive pulmonary disease (COPD) subjects. MATERIALS AND

methodsIn this retrospective study, we used TotalSegmentator to acquire 3D pectoral muscle (PM) and erector spinae muscle (ESM) volumes from chest CT, normalizing them to height and vertebral bone volume, respectively. 2D quantitative parameters were acquired and normalized. The support vector machine models were developed based on normalized 3D and 2D muscle parameters, respectively, with ten-fold cross-validation. Receiver operating characteristic curves were used to analyze the discriminative performance of models, and the SHapley Additive exPlanations algorithm was used to quantify the importance of each parameter.

results1660 subjects (616 normal, 646 PRISm, 398 COPD) (median age, 66 years [IQR: 60-72 years]; 1014 males) were included and divided into training set (n = 1162), test set (n = 498). In the test set, the 3D model (macro-average AUC, 0.735; micro-average AUC, 0.733) outperformed the 2D model (macro-average AUC, 0.646; micro-average AUC, 0.665), and 3D ESM density was the most relevant muscle parameter. 3D muscle density showed a decreasing trend from normal to PRISm to COPD. In COPD, females exhibited decreased density (difference range: -14.48 to -7.79; p < 0.001), while males had a more significant decrease in volume, especially pectoralis major volume index (difference: -11.18; 95% CI -15.57, -5.08; p < 0.001).

conclusionsThe 3D muscle parameter model demonstrated higher discriminative performance than the 2D model for discriminating normal, PRISm, and COPD subjects. KEY POINTS: Question The discriminative performance of 3D CT-based muscle quantification in subjects with impaired pulmonary function remains unclear. Findings 3D muscle analysis achieved superior discrimination of normal, PRISm, and COPD subjects, with density-related parameters showing consistent group differences, particularly ESM density. Critical relevance By comparison with conventional 2D assessment, 3D CT-based muscle quantification showed better discrimination of normal, PRISm, and COPD, which might advance the personalized precision and comprehensive assessment, including muscle alteration for subjects with impaired pulmonary function.

Indexed as

COPDCTMusclePRISmSegmentation

Identifiers

PMID42658393
PMCPMC13522301

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.