Scholarlink

<p>Diffusing capacity for carbon monoxide (DLCO) reflects pulmonary gas exchange efficiency, but its measurement and interpretation remain challenging due to physiological and technical variability. Quantitative computed tomography (CT) provides structural insights that may help address these limitations. This study investigated associations among demographic factors, spirometric values, DLCO, and CT-derived metrics in patients with various lung conditions. Additionally, we developed predictive models for DLCO. We analyzed mean lung density (MLD), percentile index 15 (PI15), percentages of low and high attenuation areas (LAA% and HAA%), emphysema size heterogeneity (D-slope), airway wall thickness (AWTPi10), and pulmonary vascular indices. Network analysis and random forest regression were employed to identify determinants and predictors of DLCO. DLCO correlated positively with weight and whole lung volume, and negatively with age, MLD variation, HAA%, and D-slope. DLCO/alveolar volume was positively associated with weight, body mass index, and lung density metrics (PI15), and negatively with LAA% and D-slope. CT-derived metrics exhibited distinct correlation patterns compared to spirometric measurements. The random forest model predicted DLCO with correlation coefficient of 0.82, an RMSE of 3.04 mL/min/mmHg, and an R<sup>2</sup> of 0.58, indicating considerable predictive performance. Integrating quantitative CT metrics improves the understanding of DLCO. Imaging-based models may enhance diagnostic precision and support personalized management strategies for pulmonary diseases.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Structural determinants of pulmonary diffusing capacity identified by network analysis and machine learning on quantitative CT

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月28日

查看全文
  • Sung Jun Chung,
  • Jiyeon Kang,
  • Deok Hee Kim,
  • Hyung Koo Kang,
  • Pamela Song,
  • Sung Soon Lee,
  • Ki Hwan Kim,
  • Youlim Kim,
  • Ji-Yong Moon,
  • Hyeon-Kyoung Koo

摘要

Diffusing capacity for carbon monoxide (DLCO) reflects pulmonary gas exchange efficiency, but its measurement and interpretation remain challenging due to physiological and technical variability. Quantitative computed tomography (CT) provides structural insights that may help address these limitations. This study investigated associations among demographic factors, spirometric values, DLCO, and CT-derived metrics in patients with various lung conditions. Additionally, we developed predictive models for DLCO. We analyzed mean lung density (MLD), percentile index 15 (PI15), percentages of low and high attenuation areas (LAA% and HAA%), emphysema size heterogeneity (D-slope), airway wall thickness (AWTPi10), and pulmonary vascular indices. Network analysis and random forest regression were employed to identify determinants and predictors of DLCO. DLCO correlated positively with weight and whole lung volume, and negatively with age, MLD variation, HAA%, and D-slope. DLCO/alveolar volume was positively associated with weight, body mass index, and lung density metrics (PI15), and negatively with LAA% and D-slope. CT-derived metrics exhibited distinct correlation patterns compared to spirometric measurements. The random forest model predicted DLCO with correlation coefficient of 0.82, an RMSE of 3.04 mL/min/mmHg, and an R2 of 0.58, indicating considerable predictive performance. Integrating quantitative CT metrics improves the understanding of DLCO. Imaging-based models may enhance diagnostic precision and support personalized management strategies for pulmonary diseases.