U-net convolutional neural network applied to progressive fibrotic interstitial lung disease: is progression at CT scan associated with a clinical outcome? - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Respiratory Medicine and Research Année : 2024

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

Résumé

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.

Dates et versions

hal-04303262 , version 1 (23-11-2023)

Identifiants

Citer

Xavier Guerra, Simon Rennotte, Catalin Fetita, Marouane Boubaya, Marie-Pierre Debray, et al.. U-net convolutional neural network applied to progressive fibrotic interstitial lung disease: is progression at CT scan associated with a clinical outcome?. Respiratory Medicine and Research, 2024, 85, pp.101058. ⟨10.1016/j.resmer.2023.101058⟩. ⟨hal-04303262⟩
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