Objectives To investigate pulmonary structural changes in patients with rheumatoid arthritis (RA) using an artificial intelligence (AI)-based CT segmentation model and evaluate the diagnostic potential of quantitative imaging biomarkers. Methods This cross-sectional study included 556 non-smoking RA patients and 472 non-smoking healthy controls. An AI-based CT segmentation model was used to quantify total lung volume (TLV), percentage of low attenuation area (LAA%-200–700), total airway volume (TAV), small airway volume (TAV2mm), total vessel volume (TVV), small vessel volume (TVV5mm), vessel tortuosity (VT), and fractal dimension (FD). Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. Subgroup analysis compared RA patients with and without respiratory symptoms. Results RA patients exhibited significantly lower TLV, increased LAA%-200–700, marked reductions in small airway volume (TAV2mm) and small vessel volume (TVV5mm), and a minimal, albeit statistically significant, alteration in VT compared to healthy controls (all p < 0.001). TAV2mm demonstrated the highest single-parameter diagnostic performance (AUC: 0.801). The combination of TAV2mm, TVV5mm, and LAA%-200–700 achieved superior discrimination (AUC: 0.837). Symptomatic RA patients showed greater reductions in vascular volumes and more extensive interstitial abnormalities than asymptomatic patients (p < 0.05), confirming clinical relevance. Conclusion RA patients exhibit quantifiable small airway loss and imaging features suggestive of vascular simplification. A combination of small airway, small vessel, and interstitial metrics effectively distinguishes RA patients from healthy controls. <Table Float="No" ID="Taba"> <tgroup cols="2"> <colspec align="left" colname="c1" colnum="1" /> <colspec align="left" colname="c2" colnum="2" /> <tbody> <row> <entry align="left" nameend="c2" namest="c1"> Key Points • Potential Imaging Signature: AI-based CT quantification identifies a potential “vascular simplification” pattern (primarily characterized by reduced TVV5mm) in RA lungs, which appears distinct from the vascular changes seen in classic pulmonary hypertension and warrants further validation. • Dual-Compartment Damage: RA patients exhibit concurrent loss of small airways (TAV2mm, AUC=0.801) and pruning of small vessels (TVV5mm), with both abnormalities correlating with interstitial changes (LAA%-200–700). • Multi-parametric Assessment: The combination of small airway, small vessel, and interstitial metrics achieves excellent discrimination (AUC=0.837), outperforming any single parameter for detecting RA associated lung involvement. </entry> </row> </tbody> </tgroup> </Table>

AI-based CT quantification reveals small airway loss and vascular simplification in rheumatoid arthritis-associated lung disease

期刊论文
发表日期：

2026年7月6日

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Tengyue Yang,
Nan Yu,
Yuwen Niu,
Hui Yuan,
Dong Han,
Shan Dang,
Doudou Pu

摘要

Objectives

To investigate pulmonary structural changes in patients with rheumatoid arthritis (RA) using an artificial intelligence (AI)-based CT segmentation model and evaluate the diagnostic potential of quantitative imaging biomarkers.

Methods

This cross-sectional study included 556 non-smoking RA patients and 472 non-smoking healthy controls. An AI-based CT segmentation model was used to quantify total lung volume (TLV), percentage of low attenuation area (LAA%-200–700), total airway volume (TAV), small airway volume (TAV2mm), total vessel volume (TVV), small vessel volume (TVV5mm), vessel tortuosity (VT), and fractal dimension (FD). Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. Subgroup analysis compared RA patients with and without respiratory symptoms.

Results

RA patients exhibited significantly lower TLV, increased LAA%-200–700, marked reductions in small airway volume (TAV2mm) and small vessel volume (TVV5mm), and a minimal, albeit statistically significant, alteration in VT compared to healthy controls (all p < 0.001). TAV2mm demonstrated the highest single-parameter diagnostic performance (AUC: 0.801). The combination of TAV2mm, TVV5mm, and LAA%-200–700 achieved superior discrimination (AUC: 0.837). Symptomatic RA patients showed greater reductions in vascular volumes and more extensive interstitial abnormalities than asymptomatic patients (p < 0.05), confirming clinical relevance.

Conclusion

RA patients exhibit quantifiable small airway loss and imaging features suggestive of vascular simplification. A combination of small airway, small vessel, and interstitial metrics effectively distinguishes RA patients from healthy controls.

Key Points

• Potential Imaging Signature: AI-based CT quantification identifies a potential “vascular simplification” pattern (primarily characterized by reduced TVV5mm) in RA lungs, which appears distinct from the vascular changes seen in classic pulmonary hypertension and warrants further validation.

• Dual-Compartment Damage: RA patients exhibit concurrent loss of small airways (TAV2mm, AUC=0.801) and pruning of small vessels (TVV5mm), with both abnormalities correlating with interstitial changes (LAA%-200–700).

• Multi-parametric Assessment: The combination of small airway, small vessel, and interstitial metrics achieves excellent discrimination (AUC=0.837), outperforming any single parameter for detecting RA associated lung involvement.