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Related Experiment Video

Updated: Apr 30, 2026

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
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Structural determinants of pulmonary diffusing capacity identified by network analysis and machine learning on

Sung Jun Chung1, Jiyeon Kang1, Deok Hee Kim1

  • 1Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Juhwa-ro 170, Ilsanseo-gu, Goyang, 10380, Republic of Korea.

Scientific Reports
|April 28, 2026
PubMed
Summary
This summary is machine-generated.

Quantitative CT scans reveal key factors influencing diffusing capacity for carbon monoxide (DLCO), a measure of lung function. These imaging insights improve DLCO prediction and personalized pulmonary disease management.

Keywords:
diffusing capacity for carbon monoxidemachine learningnetwork analysispulmonary functionquantitative computed tomographystructural biomarker

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Area of Science:

  • Pulmonary Medicine
  • Radiology
  • Medical Imaging Analysis

Background:

  • Diffusing capacity for carbon monoxide (DLCO) assesses pulmonary gas exchange but faces measurement challenges.
  • Quantitative computed tomography (CT) offers structural lung insights to complement functional tests.

Purpose of the Study:

  • To explore associations between demographic, spirometric, and quantitative CT metrics with DLCO.
  • To develop predictive models for DLCO using quantitative CT data.

Main Methods:

  • Analysis of demographic factors, spirometry, DLCO, and CT-derived metrics (MLD, PI15, LAA%, HAA%, D-slope, AWTPi10).
  • Network analysis and random forest regression to identify DLCO determinants and predictors.
  • Development and validation of a DLCO predictive model.

Main Results:

  • DLCO showed correlations with weight, lung volume, age, MLD variation, HAA%, and D-slope.
  • CT metrics had distinct correlation patterns compared to spirometry.
  • The random forest model predicted DLCO with R² = 0.58, demonstrating significant predictive power.

Conclusions:

  • Quantitative CT metrics enhance the understanding of DLCO variability in lung diseases.
  • Imaging-based predictive models show potential for improved diagnostic precision.
  • These models may support personalized management strategies for patients with pulmonary conditions.