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Value of CT quantification in progressive fibrosing interstitial lung disease: a deep learning approach

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Abstract

Objectives

To evaluate the relationship of changes in the deep learning–based CT quantification of interstitial lung disease (ILD) with changes in forced vital capacity (FVC) and visual assessments of ILD progression, and to investigate their prognostic implications.

Methods

This study included ILD patients with CT scans at intervals of over 2 years between January 2015 and June 2021. Deep learning–based texture analysis software was used to segment ILD findings on CT images (fibrosis: reticular opacity + honeycombing cysts; total ILD extent: ground-glass opacity + fibrosis). Patients were grouped according to the absolute decline of predicted FVC (< 5%, 5–10%, and ≥ 10%) and ILD progression assessed by thoracic radiologists, and their quantification results were compared among these groups. The associations between quantification results and survival were evaluated using multivariable Cox regression analysis.

Results

In total, 468 patients (239 men; 64 ± 9.5 years) were included. Fibrosis and total ILD extents more increased in patients with larger FVC decline (p < .001 in both). Patients with ILD progression had higher fibrosis and total ILD extent increases than those without ILD progression (p < .001 in both). Increases in fibrosis and total ILD extent were significant prognostic factors when adjusted for absolute FVC declines of ≥ 5% (hazard ratio [HR] 1.844, p = .01 for fibrosis; HR 2.484, p < .001 for total ILD extent) and ≥ 10% (HR 2.918, p < .001 for fibrosis; HR 3.125, p < .001 for total ILD extent).

Conclusion

Changes in ILD CT quantification correlated with changes in FVC and visual assessment of ILD progression, and they were independent prognostic factors in ILD patients.

Clinical relevance statement

Quantifying the CT features of interstitial lung disease using deep learning techniques could play a key role in defining and predicting the prognosis of progressive fibrosing interstitial lung disease.

Key Points

• Radiologic findings on high-resolution CT are important in diagnosing progressive fibrosing interstitial lung disease.

• Deep learning–based quantification results for fibrosis and total interstitial lung disease extents correlated with the decline in forced vital capacity and visual assessments of interstitial lung disease progression, and emerged as independent prognostic factors.

• Deep learning–based interstitial lung disease CT quantification can play a key role in diagnosing and prognosticating progressive fibrosing interstitial lung disease.

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Abbreviations

CI:

Confidence interval

CTD-ILD:

Connective tissue disease–associated interstitial lung disease

DLco:

Diffusing capacity of the lung for carbon monoxide

FVC:

Forced vital capacity

GGO:

Ground-glass opacity

HR:

Hazard ratio

HRCT:

High-resolution CT

ILD:

Interstitial lung disease

IQR:

Interquartile range

IPF:

Idiopathic pulmonary fibrosis

NSIP:

Nonspecific interstitial pneumonia

PF-ILD:

Progressive fibrosing interstitial lung disease

PFT:

Pulmonary function test

UIP:

Usual interstitial pneumonia

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Acknowledgements

The authors would like to acknowledge Andrew Dombrowski, Ph.D. (Compecs, Inc.) for his assistance in English editing.

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Radiology, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea

    Seok Young Koh, Jong Hyuk Lee, Hyungin Park & Jin Mo Goo

  2. Department of Radiology, Seoul National University College of Medicine, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea

    Jin Mo Goo

  3. Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea

    Jin Mo Goo

  4. Cancer Research Institute, Seoul National University, 101, Daehak-ro, Jongno-gu, Seoul, 03080, South Korea

    Jin Mo Goo

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  1. Seok Young Koh
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Corresponding author

Correspondence to Jin Mo Goo.

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Guarantor

The scientific guarantor of this publication is Jin Mo Goo.

Conflict of interest

The authors of this manuscript declare relationships with the following companies:

Activities not related to the present article—Jin Mo Goo reports research grants from LG Electronics and CoreLine Soft; Jong Hyuk Lee reports research grants from CoreLine Soft and consulting fee from Radisen.

Other authors (Seok Young Koh, Hyungin Park) have no conflicts of interest.

Statistics and biometry

One of the authors has significant statistical expertise (Jong Hyuk Lee).

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

The Institutional Review Board of Seoul National University Hospital approval was obtained (IRB No. H-2112-040-1279).

Study subjects or cohorts overlap

The study participants have not been reported before.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Seok Young Koh and Jong Hyuk Lee contributed equally to this research as first authors.

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Koh, S.Y., Lee, J.H., Park, H. et al. Value of CT quantification in progressive fibrosing interstitial lung disease: a deep learning approach. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-10483-9

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