CT-based body composition analysis and pulmonary fat attenuation volume as biomarkers to predict overall survival in patients with non-specific interstitial pneumonia.
Body composition
Deep learning
Lung diseases (interstitial)
Survival analysis
Tomography (x-ray computed)
Journal
European radiology experimental
ISSN: 2509-9280
Titre abrégé: Eur Radiol Exp
Pays: England
ID NLM: 101721752
Informations de publication
Date de publication:
14 Oct 2024
14 Oct 2024
Historique:
received:
08
04
2024
accepted:
20
09
2024
medline:
14
10
2024
pubmed:
14
10
2024
entrez:
14
10
2024
Statut:
epublish
Résumé
Non-specific interstitial pneumonia (NSIP) is an interstitial lung disease that can result in end-stage fibrosis. We investigated the influence of body composition and pulmonary fat attenuation volume (CTpfav) on overall survival (OS) in NSIP patients. In this retrospective single-center study, 71 NSIP patients with a median age of 65 years (interquartile range 21.5), 39 females (55%), who had a computed tomography from August 2009 to February 2018, were included, of whom 38 (54%) died during follow-up. Body composition analysis was performed using an open-source nnU-Net-based framework. Features were combined into: Sarcopenia (muscle/bone); Fat (total adipose tissue/bone); Myosteatosis (inter-/intra-muscular adipose tissue/total adipose tissue); Mediastinal (mediastinal adipose tissue/bone); and Pulmonary fat index (CTpfav/lung volume). Kaplan-Meier analysis with a log-rank test and multivariate Cox regression were used for survival analyses. Patients with a higher (> median) Sarcopenia and lower (< median) Mediastinal Fat index had a significantly better survival probability (2-year survival rate: 83% versus 71% for high versus low Sarcopenia index, p = 0.023; 83% versus 72% for low versus high Mediastinal fat index, p = 0.006). In univariate analysis, individuals with a higher Pulmonary fat index exhibited significantly worse survival probability (2-year survival rate: 61% versus 94% for high versus low, p = 0.003). Additionally, it was an independent risk predictor for death (hazard ratio 2.37, 95% confidence interval 1.03-5.48, p = 0.043). Fully automated body composition analysis offers interesting perspectives in patients with NSIP. Pulmonary fat index was an independent predictor of OS. The Pulmonary fat index is an independent predictor of OS in patients with NSIP and demonstrates the potential of fully automated, deep-learning-driven body composition analysis as a biomarker for prognosis estimation. This is the first study assessing the potential of CT-based body composition analysis in patients with non-specific interstitial pneumonia (NSIP). A single-center analysis of 71 patients with board-certified diagnosis of NSIP is presented Indices related to muscle, mediastinal fat, and pulmonary fat attenuation volume were significantly associated with survival at univariate analysis. CT pulmonary fat attenuation volume, normalized by lung volume, resulted as an independent predictor for death.
Sections du résumé
BACKGROUND
BACKGROUND
Non-specific interstitial pneumonia (NSIP) is an interstitial lung disease that can result in end-stage fibrosis. We investigated the influence of body composition and pulmonary fat attenuation volume (CTpfav) on overall survival (OS) in NSIP patients.
METHODS
METHODS
In this retrospective single-center study, 71 NSIP patients with a median age of 65 years (interquartile range 21.5), 39 females (55%), who had a computed tomography from August 2009 to February 2018, were included, of whom 38 (54%) died during follow-up. Body composition analysis was performed using an open-source nnU-Net-based framework. Features were combined into: Sarcopenia (muscle/bone); Fat (total adipose tissue/bone); Myosteatosis (inter-/intra-muscular adipose tissue/total adipose tissue); Mediastinal (mediastinal adipose tissue/bone); and Pulmonary fat index (CTpfav/lung volume). Kaplan-Meier analysis with a log-rank test and multivariate Cox regression were used for survival analyses.
RESULTS
RESULTS
Patients with a higher (> median) Sarcopenia and lower (< median) Mediastinal Fat index had a significantly better survival probability (2-year survival rate: 83% versus 71% for high versus low Sarcopenia index, p = 0.023; 83% versus 72% for low versus high Mediastinal fat index, p = 0.006). In univariate analysis, individuals with a higher Pulmonary fat index exhibited significantly worse survival probability (2-year survival rate: 61% versus 94% for high versus low, p = 0.003). Additionally, it was an independent risk predictor for death (hazard ratio 2.37, 95% confidence interval 1.03-5.48, p = 0.043).
CONCLUSION
CONCLUSIONS
Fully automated body composition analysis offers interesting perspectives in patients with NSIP. Pulmonary fat index was an independent predictor of OS.
RELEVANCE STATEMENT
CONCLUSIONS
The Pulmonary fat index is an independent predictor of OS in patients with NSIP and demonstrates the potential of fully automated, deep-learning-driven body composition analysis as a biomarker for prognosis estimation.
KEY POINTS
CONCLUSIONS
This is the first study assessing the potential of CT-based body composition analysis in patients with non-specific interstitial pneumonia (NSIP). A single-center analysis of 71 patients with board-certified diagnosis of NSIP is presented Indices related to muscle, mediastinal fat, and pulmonary fat attenuation volume were significantly associated with survival at univariate analysis. CT pulmonary fat attenuation volume, normalized by lung volume, resulted as an independent predictor for death.
Identifiants
pubmed: 39400764
doi: 10.1186/s41747-024-00519-0
pii: 10.1186/s41747-024-00519-0
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
114Subventions
Organisme : Deutsche Forschungsgemeinschaft
ID : FU 356/12-2
Organisme : Deutsche Forschungsgemeinschaft
ID : FU 356/12-2
Informations de copyright
© 2024. The Author(s).
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