Quantitative analysis of lung parenchymal involvement in Behçet's disease by the novel automatic program

Area of Science:

• Pulmonary Medicine
• Rheumatology
• Radiology

Background:

• Behçet's disease is a systemic vasculitis with potential multi-organ involvement.
• Pulmonary manifestations in Behçet's disease can be subtle and difficult to detect with conventional imaging.
• Quantitative analysis offers a sensitive method to detect early parenchymal changes.

Purpose of the Study:

• To quantitatively assess lung parenchymal changes in Behçet's disease patients.
• To identify early quantitative lung alterations preceding visible radiological findings.
• To correlate lung volume and density changes with disease status.

Main Methods:

• Computed tomography (CT) scans of the lungs were analyzed using an automated program.
• Evaluated parameters included total lung volume, lung density, and opacity volume percentage.
• Compared data from 31 Behçet's disease patients (17 with and 14 without radiological findings) and 33 healthy controls.

Main Results:

• Behçet's disease patients exhibited significantly reduced total lung volume (P < 0.0001) and increased mean lung density (P = 0.0023) compared to controls.
• A strong negative correlation was found between lung density and lung volume in Behçet's patients (r = -0.795, P < 0.0001).
• Significantly lower lung volume was observed in Behçet's patients without positive radiological findings compared to controls (P = 0.0342).

Conclusions:

• Lung volumetric changes are significantly impacted by Behçet's disease.
• Decreased lung volume can be an early indicator of Behçet's disease, even in the absence of apparent radiological abnormalities.
• Quantitative CT analysis is valuable for detecting subclinical pulmonary involvement in Behçet's disease.