ArticleFrontiers in cellular and infection microbiology2026
The first CT-based classification system for pulmonary actinomycosis: correlating imaging patterns with therapeutic strategies and prognosis in a highly misdiagnosed disease.
Article in Frontiers in cellular and infection microbiology, 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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Abstract
Background: Pulmonary actinomycosis is a rare, chronic, suppurative infection caused by anaerobic, Gram-positive Actinomycesspecies, characterized by a notoriously high misdiagnosis rate. Due to non-specific clinical and imaging presentations, it is frequently misdiagnosed. This study aims to establish the first CT-based classification system to improve diagnostic accuracy and guide individualized treatment. Methods: A retrospective analysis was conducted on patients with pathologically or molecularly confirmed pulmonary actinomycosis between February 2003 and April 2024. Their chest CT features were systematically analyzed to establish the classification. Results: Sixty-nine patients were enrolled (mean age 53.3 ± 12.1 years; 57 males). Based on chest CT features, the disease was categorized into five types: Type I: Nodular infiltrative (8.7%); Type II: Airspace suspension (73.9%), the most prevalent; Type III: Bilateral disseminated (2.9%), relatively rare; Type IV: Airway-originated (8.7%); Type V: Occult manifestation (5.8%). Although the postoperative complication rate was 50.0%, patients achieved a high cure rate following surgical resection combined with long-term antibiotics. The Occult manifestation type responded favorably to conservative treatment. The overall cure rate was 87.0% (60/69), with cure rates of 90.0% in surgical patients and 82.8% in non-surgical patients. Conclusion: This study introduces the first CT-based classification system for pulmonary actinomycosis, which improves preoperative diagnostic accuracy and guides therapeutic decision-making. The classification types are strongly associated with distinct treatment strategies and prognosis. Applying this system could reduce misdiagnosis and inform optimal management. Future validation in multicenter cohorts is essential to refine and generalize this framework.
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