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Prognostic value of deep learning–based fibrosis quantification on chest CT in idiopathic pulmonary fibrosis

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Abstract

Objective

To investigate the prognostic value of deep learning (DL)–driven CT fibrosis quantification in idiopathic pulmonary fibrosis (IPF).

Methods

Patients diagnosed with IPF who underwent nonenhanced chest CT and spirometry between 2005 and 2009 were retrospectively collected. Proportions of normal (CT-Norm%) and fibrotic lung volume (CT-Fib%) were calculated on CT using the DL software. The correlations of CT-Norm% and CT-Fib% with forced vital capacity (FVC) and diffusion capacity of carbon monoxide (DLCO) were evaluated. The multivariable-adjusted hazard ratios (HRs) of CT-Norm% and CT-Fib% for overall survival were calculated with clinical and physiologic variables as covariates using Cox regression. The feasibility of substituting CT-Norm% for DLCO in the GAP index was investigated using time-dependent areas under the receiver operating characteristic curve (TD-AUCs) at 3 years.

Results

In total, 161 patients (median age [IQR], 68 [62–73] years; 104 men) were evaluated. CT-Norm% and CT-Fib% showed significant correlations with FVC (Pearson’s r, 0.40 for CT-Norm% and − 0.37 for CT-Fib%; both p < 0.001) and DLCO (0.52 for CT-Norm% and − 0.46 for CT-Fib%; both p < 0.001). On multivariable Cox regression, both CT-Norm% and CT-Fib% were independent prognostic factors when adjusted to age, sex, smoking status, comorbid chronic diseases, FVC, and DLCO (HRs, 0.98 [95% CI 0.97–0.99; p < 0.001] for CT-Norm% at 3 years and 1.03 [1.01–1.05; p = 0.01] for CT-Fib%). Substituting CT-Norm% for DLCO showed comparable discrimination to the original GAP index (TD-AUC, 0.82 [0.78–0.85] vs. 0.82 [0.79–0.86]; p = 0.75).

Conclusion

CT-Norm% and CT-Fib% calculated using chest CT–based deep learning software were independent prognostic factors for overall survival in IPF.

Key Points

• Normal and fibrotic lung volume proportions were automatically calculated using commercial deep learning software from chest CT taken from 161 patients diagnosed with idiopathic pulmonary fibrosis.

• CT-quantified volumetric parameters from commercial deep learning software were correlated with forced vital capacity (Pearson’s r, 0.40 for normal and − 0.37 for fibrotic lung volume proportions) and diffusion capacity of carbon monoxide (Pearson’s r, 0.52 and − 0.46, respectively).

• Normal and fibrotic lung volume proportions (hazard ratios, 0.98 and 1.04; both p < 0.001) independently predicted overall survival when adjusted for clinical and physiologic variables.

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Abbreviations

ATS/ERS/JRS/ALAT:

American Thoracic Society/European Respiratory Society/Japanese Respiratory Society/Latin American Thoracic Association

CT-Fib%:

Fibrotic lung volume proportion

CT-Norm%:

Normal lung volume proportion

DLCO :

Diffusion capacity of carbon monoxide

DLCO%:

Diffusion capacity of carbon monoxide, expressed as a percentage of the predicted value

FEV1%:

Forced expiratory volume in 1 s, expressed as a percentage of the predicted value

FVC:

Forced vital capacity

FVC%:

Forced vital capacity, expressed as a percentage of the predicted value

GAP:

Gender, age, and physiology

IPF:

Idiopathic pulmonary fibrosis

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Funding

This study has received funding by the Research Program 2020 funded by Seoul National University College of Medicine Research Foundation (800-20200313), the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2020R1C1C1003684), and the grant from the Seoul National University Hospital Research Fund (04-2020-2040). However, the funders had no role in the study design; in the collection, analysis, and interpretation of the data; in the writing of the report; and in the decision to submit the article for publication.

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Authors and Affiliations

  1. Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea

    Ju Gang Nam, Soon Ho Yoon, Jin Mo Goo & Hyungjin Kim

  2. Medical Research Collaborating Center, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea

    Yunhee Choi

  3. Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea

    Sang-Min Lee

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

    Jin Mo Goo

  5. Cancer Research Institute, Seoul National University, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea

    Jin Mo Goo

Authors
  1. Ju Gang Nam
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Corresponding author

Correspondence to Hyungjin Kim.

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Guarantor

The scientific guarantor of this publication is Hyungjin Kim.

Conflict of interest

Author Hyungjin Kim is a member of European Radiology Scientific Editorial board. He has not taken part in review or selection process for this manuscript. The remaining authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Statistical analyses were performed by a statistician with 15 years of experience (Yunhee Choi). She is one of the co-authors.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

The patients in our study have been reported in previous publications on validation of the GAP score and lung volume estimation using chest radiographs.

Kim ES, Choi SM, Lee J, et al Validation of the GAP score in Korean patients with idiopathic pulmonary fibrosis. Chest. 2015;147(2):430–437. https://doi.org/10.1378/chest.14-0453; and in submitted: Deep Learning for Estimating Lung Capacity on Chest Radiographs to Predict Survival in Idiopathic Pulmonary Fibrosis as PDF in file inventory.

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Nam, J.G., Choi, Y., Lee, SM. et al. Prognostic value of deep learning–based fibrosis quantification on chest CT in idiopathic pulmonary fibrosis. Eur Radiol 33, 3144–3155 (2023). https://doi.org/10.1007/s00330-023-09534-y

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  • DOI: https://doi.org/10.1007/s00330-023-09534-y

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