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Structural determinants of pulmonary diffusing capacity identified by network analysis and machine learning on quantitative CT
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  • Published: 28 April 2026

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

  • Sung Jun Chung1,
  • Jiyeon Kang1,
  • Deok Hee Kim1,
  • Hyung Koo Kang1,
  • Pamela Song2,
  • Sung Soon Lee1,
  • Ki Hwan Kim3,
  • Youlim Kim4,
  • Ji-Yong Moon4 &
  • …
  • Hyeon-Kyoung Koo1 

Scientific Reports , Article number:  (2026) Cite this article

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Subjects

  • Biomarkers
  • Medical research

Abstract

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.

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Abbreviations

AWTPi10:

Airway wall thickness at an internal perimeter of 10 mm

BMI:

Body mass index

BV5:

Blood volume in vessels with a cross-sectional area of ≤ 5 mm2

BV5/TBV:

Ratio of small blood vessel volume (≤ 5 mm) to total blood volume

CT:

Computed tomography

DLCO:

Diffusing capacity for carbon monoxide

DLCO/VA:

Diffusing capacity for carbon monoxide corrected for alveolar volume

FEV1 :

Forced expiratory volume in one second

FEV1/FVC:

Ratio of forced expiratory volume in one second to forced vital capacity

FVC:

Forced vital capacity

HAA:

High-attenuation area

LAA:

Low-attenuation area

MLD:

Mean lung density

TBV:

Total blood volume

V/Q:

Ventilation-perfusion ratio

Acknowledgments

The authors gratefully acknowledge the contributions of the laboratory staff—Mikyung Lee, Juhyeon Park, and Dayoung Han—for their assistance in conducting the measurements.

Funding

This work was supported by the National Research Foundation of Korea (NIRF) grant funded by the Korean government (MSIT: No. RS-2024-00359875). The funding bodies played no role in the study design, data collection, analysis, interpretation, and writing of the manuscript.

Author information

Authors and Affiliations

  1. Division 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

    Sung Jun Chung, Jiyeon Kang, Deok Hee Kim, Hyung Koo Kang, Sung Soon Lee & Hyeon-Kyoung Koo

  2. Department of Neurology, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea

    Pamela Song

  3. Department of Radiology, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea

    Ki Hwan Kim

  4. Division of Pulmonary and Allergy, Department of Internal Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea

    Youlim Kim & Ji-Yong Moon

Authors
  1. Sung Jun Chung
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  2. Jiyeon Kang
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  3. Deok Hee Kim
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  4. Hyung Koo Kang
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  5. Pamela Song
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  6. Sung Soon Lee
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  7. Ki Hwan Kim
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  8. Youlim Kim
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  9. Ji-Yong Moon
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  10. Hyeon-Kyoung Koo
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Corresponding author

Correspondence to Hyeon-Kyoung Koo.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Ethics approval and consent to participate

This study was conducted in accordance with the principles of the Declaration of Helsinki. The ethics committee of Ilsan Paik Hospital approved the study (IRB No. 2025-02-008).

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Not applicable.

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Cite this article

Chung, S.J., Kang, J., Kim, D.H. et al. Structural determinants of pulmonary diffusing capacity identified by network analysis and machine learning on quantitative CT. Sci Rep (2026). https://doi.org/10.1038/s41598-026-51056-2

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  • Received: 04 April 2025

  • Accepted: 25 April 2026

  • Published: 28 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-51056-2

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Keywords

  • diffusing capacity for carbon monoxide
  • quantitative computed tomography
  • pulmonary function
  • network analysis
  • machine learning
  • structural biomarker
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