Background: High-resolution chest computed tomography (CT) is a crucial assessment tool for diagnosing Birt-Hogg-Dubé (BHD) syndrome. This study aimed to analyze differences of lung cysts between BHD and other cystic lung diseases. Methods: From January 2020 to December 2022, patients with multiple lung cysts who underwent chest CT at Gangnam Severance Hospital were included. Results: Over a 3-year period (from January 2020 to December 2022), out of 52,823 patients who underwent a chest CT scan, 301 (0.6%) patients with multiple lung cysts were enrolled in this study. Of enrolled patients, 24 (8.0%) were diagnosed with BHD. In patients with BHD, 95.8% exhibited bilateral cysts, and 83.3% showed basal predominance. The cysts’ maximal diameter averaged 32.1 mm (interquartile range, 26.5 to 43.5). Additionally, 95.8% of patients with BHD had diverse cyst sizes and morphologies. Multivariate logistic regression analysis revealed that bilateral cysts (odds ratio [OR], 12.393; 95% confidence interval [CI], 1.613 to 274.682; p=0.038), basal predominance (OR, 8.511; 95% CI, 2.252 to 39.392; p=0.002), maximum diameter (OR, 1.053; 95% CI, 1.009 to 1.108; p=0.032), and diversity of morphology (OR, 19.513; 95% CI, 2.833 to 398.119; p=0.010) were significant factors associated with BHD diagnosis. A multivariate prediction model for BHD diagnosis demonstrated a sensitivity of 95.83%, a specificity of 81.22%, and an area under the receiver operating characteristic curve of 0.951 (95% CI, 0.914 to 0.987). Conclusion Distinguishing features of lung cysts from other cystic lung diseases include bilateral cysts, basal dominance, large size, and irregular shape.

Background: High-resolution chest computed tomography (CT) is a crucial assessment tool for diagnosing Birt-Hogg-Dubé (BHD) syndrome. This study aimed to analyze differences of lung cysts between BHD and other cystic lung diseases. Methods: From January 2020 to December 2022, patients with multiple lung cysts who underwent chest CT at Gangnam Severance Hospital were included. Results: Over a 3-year period (from January 2020 to December 2022), out of 52,823 patients who underwent a chest CT scan, 301 (0.6%) patients with multiple lung cysts were enrolled in this study. Of enrolled patients, 24 (8.0%) were diagnosed with BHD. In patients with BHD, 95.8% exhibited bilateral cysts, and 83.3% showed basal predominance. The cysts’ maximal diameter averaged 32.1 mm (interquartile range, 26.5 to 43.5). Additionally, 95.8% of patients with BHD had diverse cyst sizes and morphologies. Multivariate logistic regression analysis revealed that bilateral cysts (odds ratio [OR], 12.393; 95% confidence interval [CI], 1.613 to 274.682; p=0.038), basal predominance (OR, 8.511; 95% CI, 2.252 to 39.392; p=0.002), maximum diameter (OR, 1.053; 95% CI, 1.009 to 1.108; p=0.032), and diversity of morphology (OR, 19.513; 95% CI, 2.833 to 398.119; p=0.010) were significant factors associated with BHD diagnosis. A multivariate prediction model for BHD diagnosis demonstrated a sensitivity of 95.83%, a specificity of 81.22%, and an area under the receiver operating characteristic curve of 0.951 (95% CI, 0.914 to 0.987). Conclusion Distinguishing features of lung cysts from other cystic lung diseases include bilateral cysts, basal dominance, large size, and irregular shape.

Background: High-resolution chest computed tomography (CT) is a crucial assessment tool for diagnosing Birt-Hogg-Dubé (BHD) syndrome. This study aimed to analyze differences of lung cysts between BHD and other cystic lung diseases. Methods: From January 2020 to December 2022, patients with multiple lung cysts who underwent chest CT at Gangnam Severance Hospital were included. Results: Over a 3-year period (from January 2020 to December 2022), out of 52,823 patients who underwent a chest CT scan, 301 (0.6%) patients with multiple lung cysts were enrolled in this study. Of enrolled patients, 24 (8.0%) were diagnosed with BHD. In patients with BHD, 95.8% exhibited bilateral cysts, and 83.3% showed basal predominance. The cysts’ maximal diameter averaged 32.1 mm (interquartile range, 26.5 to 43.5). Additionally, 95.8% of patients with BHD had diverse cyst sizes and morphologies. Multivariate logistic regression analysis revealed that bilateral cysts (odds ratio [OR], 12.393; 95% confidence interval [CI], 1.613 to 274.682; p=0.038), basal predominance (OR, 8.511; 95% CI, 2.252 to 39.392; p=0.002), maximum diameter (OR, 1.053; 95% CI, 1.009 to 1.108; p=0.032), and diversity of morphology (OR, 19.513; 95% CI, 2.833 to 398.119; p=0.010) were significant factors associated with BHD diagnosis. A multivariate prediction model for BHD diagnosis demonstrated a sensitivity of 95.83%, a specificity of 81.22%, and an area under the receiver operating characteristic curve of 0.951 (95% CI, 0.914 to 0.987). Conclusion Distinguishing features of lung cysts from other cystic lung diseases include bilateral cysts, basal dominance, large size, and irregular shape.

Background: High-resolution chest computed tomography (CT) is a crucial assessment tool for diagnosing Birt-Hogg-Dubé (BHD) syndrome. This study aimed to analyze differences of lung cysts between BHD and other cystic lung diseases. Methods: From January 2020 to December 2022, patients with multiple lung cysts who underwent chest CT at Gangnam Severance Hospital were included. Results: Over a 3-year period (from January 2020 to December 2022), out of 52,823 patients who underwent a chest CT scan, 301 (0.6%) patients with multiple lung cysts were enrolled in this study. Of enrolled patients, 24 (8.0%) were diagnosed with BHD. In patients with BHD, 95.8% exhibited bilateral cysts, and 83.3% showed basal predominance. The cysts’ maximal diameter averaged 32.1 mm (interquartile range, 26.5 to 43.5). Additionally, 95.8% of patients with BHD had diverse cyst sizes and morphologies. Multivariate logistic regression analysis revealed that bilateral cysts (odds ratio [OR], 12.393; 95% confidence interval [CI], 1.613 to 274.682; p=0.038), basal predominance (OR, 8.511; 95% CI, 2.252 to 39.392; p=0.002), maximum diameter (OR, 1.053; 95% CI, 1.009 to 1.108; p=0.032), and diversity of morphology (OR, 19.513; 95% CI, 2.833 to 398.119; p=0.010) were significant factors associated with BHD diagnosis. A multivariate prediction model for BHD diagnosis demonstrated a sensitivity of 95.83%, a specificity of 81.22%, and an area under the receiver operating characteristic curve of 0.951 (95% CI, 0.914 to 0.987). Conclusion Distinguishing features of lung cysts from other cystic lung diseases include bilateral cysts, basal dominance, large size, and irregular shape.

Background: High-resolution chest computed tomography (CT) is a crucial assessment tool for diagnosing Birt-Hogg-Dubé (BHD) syndrome. This study aimed to analyze differences of lung cysts between BHD and other cystic lung diseases. Methods: From January 2020 to December 2022, patients with multiple lung cysts who underwent chest CT at Gangnam Severance Hospital were included. Results: Over a 3-year period (from January 2020 to December 2022), out of 52,823 patients who underwent a chest CT scan, 301 (0.6%) patients with multiple lung cysts were enrolled in this study. Of enrolled patients, 24 (8.0%) were diagnosed with BHD. In patients with BHD, 95.8% exhibited bilateral cysts, and 83.3% showed basal predominance. The cysts’ maximal diameter averaged 32.1 mm (interquartile range, 26.5 to 43.5). Additionally, 95.8% of patients with BHD had diverse cyst sizes and morphologies. Multivariate logistic regression analysis revealed that bilateral cysts (odds ratio [OR], 12.393; 95% confidence interval [CI], 1.613 to 274.682; p=0.038), basal predominance (OR, 8.511; 95% CI, 2.252 to 39.392; p=0.002), maximum diameter (OR, 1.053; 95% CI, 1.009 to 1.108; p=0.032), and diversity of morphology (OR, 19.513; 95% CI, 2.833 to 398.119; p=0.010) were significant factors associated with BHD diagnosis. A multivariate prediction model for BHD diagnosis demonstrated a sensitivity of 95.83%, a specificity of 81.22%, and an area under the receiver operating characteristic curve of 0.951 (95% CI, 0.914 to 0.987). Conclusion Distinguishing features of lung cysts from other cystic lung diseases include bilateral cysts, basal dominance, large size, and irregular shape.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.