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Guerra X, Rennotte S, Fetita C, Boubaya M, Debray MP, Israël-Biet D, Bernaudin JF, Valeyre D, Cadranel J, Naccache JM, Nunes H, Brillet PY. U-net convolutional neural network applied to progressive fibrotic interstitial lung disease: Is progression at CT scan associated with a clinical outcome? Respir Med Res 2023; 85:101058. [PMID: 38141579 DOI: 10.1016/j.resmer.2023.101058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/18/2023] [Accepted: 10/17/2023] [Indexed: 12/25/2023]
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.
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Affiliation(s)
- Xavier Guerra
- Department of Radiology, Avicenne Hospital, Assistance Publique - Hôpitaux de Paris, Bobigny, France.
| | - Simon Rennotte
- Samovar Laboratory, Télécom SudParis, Institut Polytechnique de Paris, Evry, France
| | - Catalin Fetita
- Samovar Laboratory, Télécom SudParis, Institut Polytechnique de Paris, Evry, France
| | - Marouane Boubaya
- Clinical Research Unit, Avicenne Hospital, Assistance Publique - Hôpitaux de Paris, Sorbonne Paris-Nord, Bobigny, France
| | - Marie-Pierre Debray
- Department of Radiology, Bichat-Claude Bernard Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Dominique Israël-Biet
- Department of Pulmonology, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France; Université Paris - Cité, Paris, France
| | - Jean-François Bernaudin
- INSERM UMR 1272 Hypoxie & Poumon SMBH, Université Sorbonne Paris - Nord, Bobigny, France; Medicine Sorbonne Université, Paris, France
| | - Dominique Valeyre
- INSERM UMR 1272 Hypoxie & Poumon SMBH, Université Sorbonne Paris - Nord, Bobigny, France; Department of Pulmonology, Avicenne Hospital, Assistance Publique - Hôpitaux de Paris, Bobigny, France
| | - Jacques Cadranel
- Medicine Sorbonne Université, Paris, France; Department of Pulmonology, Tenon Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Jean-Marc Naccache
- Department of Pulmonology, Groupe Hospitalier Paris Saint Joseph, Paris, France
| | - Hilario Nunes
- INSERM UMR 1272 Hypoxie & Poumon SMBH, Université Sorbonne Paris - Nord, Bobigny, France; Department of Pulmonology, Avicenne Hospital, Assistance Publique - Hôpitaux de Paris, Bobigny, France
| | - Pierre-Yves Brillet
- Department of Radiology, Avicenne Hospital, Assistance Publique - Hôpitaux de Paris, Bobigny, France; INSERM UMR 1272 Hypoxie & Poumon SMBH, Université Sorbonne Paris - Nord, Bobigny, France
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