AI Article Synopsis
- High-resolution chest CT scans are essential for diagnosing Birt-Hogg-Dubé syndrome (BHD) and this study investigates how lung cysts in BHD differ from those in other cystic lung diseases.
- In a retrospective analysis of 52,823 patients, 301 were identified with multiple lung cysts, with 24 (8%) diagnosed with BHD; most BHD patients showed bilateral cysts and larger sizes compared to other conditions.
- The study established a predictive model with high sensitivity (95.83%) and specificity (81.22%) to identify BHD based on characteristics like cyst location, size, and diversity, aiding in the diagnosis of previously undetected cases.
Article Abstract
Background: High-resolution chest computed tomography (CT) is a crucial assessment tool for diagnosis of Birt-Hogg-Dubé syndrome (BHD). This study aims to analyze the differences of lung cyst between BHD and other cystic lung diseases.
Methods: From January 2020 to December 2022, we retrospectively screened all patients who underwent chest CT at Gangnam Severance Hospital. We included the patients with multiple lung cysts for the analysis of chest CT images.
Results: Over a three-year period, out of 52,823 patients who underwent a chest CT scan, 301 patients (0.6%) with multiple lung cysts were enrolled, of which 24 (8.0%) were diagnosed with BHD. Notably, 95.8% and 83.3% of BHD patients exhibited bilateral cysts and basal predominance, and had larger cysts with a maximal diameter (averaging 32.1mm [interquartile range 26.5mm to 43.5mm]) than lymphangioleiomyomatosis (17.0mm [13.2;19.1], p<0.001) and others' group (11.3mm [7.9;17.0], p<0.001). Additionally, 95.8% of BHD patients has a diverse range in cyst sizes and morphologies. Multivariate logistic regression analysis identified bilateral cysts (OR 12.393, 95% CI: 1.613-274.682, p=0.038), basal predominance (OR 8.511, 95% CI: 2.252-39.392, p=0.002), maximum diameter (OR 1.053, 95% CI: 1.009-1.108, p=0.032), and diversity of morphology (OR 19.513, 95% CI: 2.833-398.119, p=0.010) as factors associated with BHD diagnosis. By stepwise selection, a multivariate prediction model for BHD diagnosis was established, demonstrating a sensitivity of 95.83%, a specificity of 81.22%, and an AUC of 0.951 (95% CI: 0.914-0.987).
Conclusion: Distinguishing features of lung cyst from other cystic lung diseases include bilateral cysts, basal dominance, large size, and irregular shape. The predictive model can assist in identifying undiagnosed patients with BHD.
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| http://dx.doi.org/10.4046/trd.2024.0045 | DOI Listing |
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