U-net convolutional neural network applied to progressive fibrotic interstitial lung disease: Is progression at CT scan associated with a clinical outcome?

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  • Additional Information
    • Source:
      Publisher: Elsevier Masson SAS Country of Publication: France NLM ID: 101746324 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2590-0412 (Electronic) Linking ISSN: 25900412 NLM ISO Abbreviation: Respir Med Res Subsets: MEDLINE
    • Publication Information:
      Original Publication: [Issy-les-Moulineaux] : Elsevier Masson SAS, [2019]-
    • Subject Terms:
      Disease Progression* ; Tomography, X-Ray Computed*/methods ; Neural Networks, Computer* ; Lung Diseases, Interstitial*/diagnostic imaging ; Lung Diseases, Interstitial*/mortality; Humans ; Female ; Male ; Middle Aged ; Retrospective Studies ; Aged ; Prognosis ; Follow-Up Studies
    • Abstract:
      Background: Computational advances in artificial intelligence have led to the recent emergence of U-Net convolutional neural networks (CNNs) applied to medical imaging. Our objectives were to assess the progression of fibrotic interstitial lung disease (ILD) using routine CT scans processed by a U-Net CNN developed by our research team, and to identify a progression threshold indicative of poor prognosis.
      Methods: CT scans and clinical history of 32 patients with idiopathic fibrotic ILDs were retrospectively reviewed. Successive CT scans were processed by the U-Net CNN and ILD quantification was obtained. Correlation between ILD and FVC changes was assessed. ROC curve was used to define a threshold of ILD progression rate (PR) to predict poor prognostic (mortality or lung transplantation). The PR threshold was used to compare the cohort survival with Kaplan Mayer curves and log-rank test.
      Results: The follow-up was 3.8 ± 1.5 years encompassing 105 CT scans, with 3.3 ± 1.1 CT scans per patient. A significant correlation between ILD and FVC changes was obtained (p = 0.004, ρ = -0.30 [95% CI: -0.16 to -0.45]). Sixteen patients (50%) experienced unfavorable outcome including 13 deaths and 3 lung transplantations. ROC curve analysis showed an aera under curve of 0.83 (p < 0.001), with an optimal cut-off PR value of 4%/year. Patients exhibiting a PR ≥ 4%/year during the first two years had a poorer prognosis (p = 0.001).
      Conclusions: Applying a U-Net CNN to routine CT scan allowed identifying patients with a rapid progression and unfavorable outcome.
      Competing Interests: Declaration of Competing Interest XG, SR, CF, MB, DIBI, JFB, DV, JCj, JMN, PYB have no conflicts of interest. MPD received fees (presentations or participation in expert groups) from Boehringer-Ingelheim. HN received fees from Roche/Genentech, Boehringer-Ingelheim, Galapagos.
      (Copyright © 2023 SPLF and Elsevier Masson SAS. All rights reserved.)
    • Contributed Indexing:
      Keywords: Interstitial lung disease; Neural networks (computer); Progression disease; Pulmonary fibrosis
    • Publication Date:
      Date Created: 20231223 Date Completed: 20240614 Latest Revision: 20240614
    • Publication Date:
      20240615
    • Accession Number:
      10.1016/j.resmer.2023.101058
    • Accession Number:
      38141579