Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients
Abstract 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. Metho...
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2025-01-01
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| Series: | Respiratory Research |
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| Online Access: | https://doi.org/10.1186/s12931-025-03117-9 |
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| author | Julien Guiot Monique Henket Fanny Gester Béatrice André Benoit Ernst Anne-Noelle Frix Dirk Smeets Simon Van Eyndhoven Katerina Antoniou Lennart Conemans Janine Gote-Schniering Hans Slabbynck Michael Kreuter Jacobo Sellares Ioannis Tomos Guang Yang Clio Ribbens Renaud Louis Vincent Cottin Sara Tomassetti Vanessa Smith Simon L. F. Walsh |
| author_facet | Julien Guiot Monique Henket Fanny Gester Béatrice André Benoit Ernst Anne-Noelle Frix Dirk Smeets Simon Van Eyndhoven Katerina Antoniou Lennart Conemans Janine Gote-Schniering Hans Slabbynck Michael Kreuter Jacobo Sellares Ioannis Tomos Guang Yang Clio Ribbens Renaud Louis Vincent Cottin Sara Tomassetti Vanessa Smith Simon L. F. Walsh |
| author_sort | Julien Guiot |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-4192b42631e4430d8d929a958851b5af |
| institution | Kabale University |
| issn | 1465-993X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Respiratory Research |
| spelling | doaj-art-4192b42631e4430d8d929a958851b5af2025-01-26T12:48:56ZengBMCRespiratory Research1465-993X2025-01-012611910.1186/s12931-025-03117-9Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patientsJulien Guiot0Monique Henket1Fanny Gester2Béatrice André3Benoit Ernst4Anne-Noelle Frix5Dirk Smeets6Simon Van Eyndhoven7Katerina Antoniou8Lennart Conemans9Janine Gote-Schniering10Hans Slabbynck11Michael Kreuter12Jacobo Sellares13Ioannis Tomos14Guang Yang15Clio Ribbens16Renaud Louis17Vincent Cottin18Sara Tomassetti19Vanessa Smith20Simon L. F. Walsh21Department of Respiratory Medicine, University Hospital of LiègeDepartment of Respiratory Medicine, University Hospital of LiègeDepartment of Respiratory Medicine, University Hospital of LiègeDepartment of Rheumatology, University Hospital of LiègeDepartment of Respiratory Medicine, University Hospital of LiègeDepartment of Respiratory Medicine, University Hospital of LiègeicometrixicometrixLaboratory of Cellular and Molecular Pneumonology, School of Medicine, University of CreteDepartment of Respiratory Medicine, Maastricht University Medical CentreDepartment of Rheumatology and Immunology, Department of Pulmonary Medicine, Inselspital, Bern University Hospital, University of BernDepartment of Pneumology, ZNA MiddelheimMainz 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 MainzDepartment of Pneumology, Hospital Clínic-Universitat de BarcelonaDepartment of Pulmonary Medicine, SOTIRIA Chest Diseases Hospital of AthensBioengineering Department and Imperial-X, Imperial College LondonDepartment of Rheumatology, University Hospital of LiègeDepartment of Respiratory Medicine, University Hospital of LiègeNational Reference Centre for Rare Pulmonary Diseases, Louis Pradel Hospital, member of ERN-LUNG, Hospices Civils de Lyon, UMR 754, INRAE, Claude Bernard University Lyon 1Unit of Interventional Pulmonology, Department of Experimental and Clinical Medicine, Careggi University HospitalDepartment of Rheumatology, Ghent University HospitalNational Heart and Lung Institute, Imperial College LondonAbstract 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.https://doi.org/10.1186/s12931-025-03117-9Systemic sclerosisInterstitial lung diseaseArtificial intelligenceComputed tomographyPulmonary function tests |
| spellingShingle | Julien Guiot Monique Henket Fanny Gester Béatrice André Benoit Ernst Anne-Noelle Frix Dirk Smeets Simon Van Eyndhoven Katerina Antoniou Lennart Conemans Janine Gote-Schniering Hans Slabbynck Michael Kreuter Jacobo Sellares Ioannis Tomos Guang Yang Clio Ribbens Renaud Louis Vincent Cottin Sara Tomassetti Vanessa Smith Simon L. F. Walsh Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients Respiratory Research Systemic sclerosis Interstitial lung disease Artificial intelligence Computed tomography Pulmonary function tests |
| title | Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients |
| title_full | Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients |
| title_fullStr | Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients |
| title_full_unstemmed | Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients |
| title_short | Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients |
| title_sort | automated ai based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients |
| topic | Systemic sclerosis Interstitial lung disease Artificial intelligence Computed tomography Pulmonary function tests |
| url | https://doi.org/10.1186/s12931-025-03117-9 |
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