Artificial intelligence; Computed tomography; Interstitial lung disease; Pulmonary function tests; Systemic sclerosis; Humans; Female; Male; Middle Aged; Retrospective Studies; Aged; Tomography, X-Ray Computed/methods; Predictive Value of Tests; Cohort Studies; Adult; Respiratory Function Tests/methods; Lung/diagnostic imaging; Lung/physiopathology; Scleroderma, Systemic/complications; Scleroderma, Systemic/diagnostic imaging; Lung Diseases, Interstitial/diagnostic imaging; Lung Diseases, Interstitial/physiopathology; Lung Diseases, Interstitial/diagnosis; Artificial Intelligence; Lung; Lung Diseases, Interstitial; Respiratory Function Tests; Scleroderma, Systemic; Tomography, X-Ray Computed; Pulmonary and Respiratory Medicine
Abstract :
[en] [en] BACKGROUND: Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapidly evolving interstitial lung disease (ILD), driving its mortality. Specific imaging-based biomarkers associated with the evolution of lung disease are needed to help predict and quantify ILD.
METHODS: We evaluated the potential of an automated ILD quantification system (icolung®) from chest CT scans, to help in quantification and prediction of ILD progression in SSc-ILD. We used a retrospective cohort of 75 SSc-ILD patients to evaluate the potential of the AI-based quantification tool and to correlate image-based quantification with pulmonary function tests and their evolution over time.
RESULTS: We evaluated a group of 75 patients suffering from SSc-ILD, either limited or diffuse, of whom 30 presented progressive pulmonary fibrosis (PPF). The patients presenting PPF exhibited more extensive lesions (in % of total lung volume (TLV)) based on image analysis than those without PPF: 3.93 (0.36-8.12)* vs. 0.59 (0.09-3.53) respectively, whereas pulmonary functional test showed a reduction in Force Vital Capacity (FVC)(pred%) in patients with PPF compared to the others : 77 ± 20% vs. 87 ± 19% (p < 0.05). Modifications of FVC and diffusing capacity of the lungs for carbon monoxide (DLCO) over time were correlated with longitudinal radiological ILD modifications (r=-0.40, p < 0.01; r=-0.40, p < 0.01 respectively).
CONCLUSION: AI-based automatic quantification of lesions from chest-CT images in SSc-ILD is correlated with physiological parameters and can help in disease evaluation. Further clinical multicentric validation is necessary in order to confirm its potential in the prediction of patient's outcome and in treatment management.
Disciplines :
Cardiovascular & respiratory systems
Author, co-author :
Guiot, Julien ; Université de Liège - ULiège > Département des sciences cliniques > Pneumologie - Allergologie
Henket, Monique ; Centre Hospitalier Universitaire de Liège - CHU > > Service de pneumologie - allergologie
GESTER, Fanny ; Centre Hospitalier Universitaire de Liège - CHU > > Service de pneumologie - allergologie
André, Béatrice ; Université de Liège - ULiège > Département des sciences cliniques
Antoniou, Katerina; Laboratory of Cellular and Molecular Pneumonology, School of Medicine, University of Crete, Heraklion, Crete, Greece
Conemans, Lennart; Department of Respiratory Medicine, Maastricht University Medical Centre, Maastricht, the Netherlands
Gote-Schniering, Janine; Department of Rheumatology and Immunology, Department of Pulmonary Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland ; Department for BioMedical Research (DBMR), Lung Precision Medicine (LPM), University of Bern, Bern, Switzerland
Slabbynck, Hans; Department of Pneumology, ZNA Middelheim, Antwerpen, Belgium
Kreuter, Michael; Mainz Center for Pulmonary Medicine, Department of Pneumology, Department of Pulmonary, ZfT, Mainz University Medical Center and Department of Pulmonary, Critical Care and Sleep Medicine, Marienhaus Clinic Mainz, Mainz, Germany
Sellares, Jacobo; Department of Pneumology, Hospital Clínic-Universitat de Barcelona, Barcelona, Spain
Tomos, Ioannis; Department of Pulmonary Medicine, SOTIRIA Chest Diseases Hospital of Athens, Athens, Greece
Yang, Guang; Bioengineering Department and Imperial-X, Imperial College London, London, UK ; National Heart and Lung Institute, Imperial College London, London, UK
Ribbens, Clio ; Université de Liège - ULiège > Département des sciences cliniques > Rhumatologie
Louis, Renaud ; Université de Liège - ULiège > Département des sciences cliniques > Pneumologie - Allergologie
Cottin, Vincent; National Reference Centre for Rare Pulmonary Diseases, Louis Pradel Hospital, member of ERN-LUNG, Hospices Civils de Lyon, UMR 754, INRAE, Claude Bernard University Lyon 1, Lyon, France
Tomassetti, Sara; Unit of Interventional Pulmonology, Department of Experimental and Clinical Medicine, Careggi University Hospital, Florence, Italy
Smith, Vanessa; Department of Rheumatology, Ghent University Hospital, Ghent, Belgium ; Department of Internal Medicine, Ghent University, Ghent, Belgium ; Unit for Molecular Immunology and Inflammation, VIB Inflammation Research Centre (IRC), Ghent, Belgium
Walsh, Simon L F; National Heart and Lung Institute, Imperial College London, London, UK
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