Cavitary Lung Diseases: A Clinical-Radiologic Algorithmic Approach.

Area of Science:

• Radiology
• Pulmonology
• Pathology

Background:

• Thoracic cavities on imaging have a broad differential diagnosis.
• Infections, autoimmune conditions, and malignancies are key considerations.
• A systematic approach is needed for accurate diagnosis.

Purpose of the Study:

• To offer an algorithmic approach for evaluating thoracic cavities.
• To classify potential causes based on clinical and radiographic timelines.
• To improve diagnostic confidence by integrating imaging with clinical context.

Main Methods:

• Exclusion of imaging mimics.
• Classification of cavities based on duration of symptoms and radiographic findings (<12 weeks vs. ≥12 weeks).
• Integration of imaging findings with clinical context.

Main Results:

• Acute/subacute processes (<12 weeks) suggest bacterial, nocardial, or fungal causes (abscesses, necrotizing pneumonias, septic emboli).
• Chronic processes (≥12 weeks) suggest mycobacterial, fungal, viral, parasitic infections, malignancy, or autoimmune disorders.
• Radiographic features alone often lack specificity.

Conclusions:

• An algorithmic approach aids in evaluating thoracic cavities.
• Duration of symptoms and radiographic abnormalities are crucial for differential diagnosis.
• Combining imaging with clinical context is essential for confident diagnosis.