Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis
Introduction Systemic sclerosis (SSc) is a complex inflammatory vasculopathy with diverse symptoms and variable disease progression. Despite its known impact on body composition (BC), clinical decision-making has yet to incorporate these biomarkers. This study aims to extract quantitative BC imaging...
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BMJ Publishing Group
2025-06-01
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| Online Access: | https://rmdopen.bmj.com/content/11/2/e005090.full |
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| author | Gabriela Riemekasten Felix Nensa Hanna Grasshoff René Hosch Malte Maria Sieren Lennart Berkel Jörg Barkhausen Roman Kloeckner Franz Wegner |
| author_facet | Gabriela Riemekasten Felix Nensa Hanna Grasshoff René Hosch Malte Maria Sieren Lennart Berkel Jörg Barkhausen Roman Kloeckner Franz Wegner |
| author_sort | Gabriela Riemekasten |
| collection | DOAJ |
| description | Introduction Systemic sclerosis (SSc) is a complex inflammatory vasculopathy with diverse symptoms and variable disease progression. Despite its known impact on body composition (BC), clinical decision-making has yet to incorporate these biomarkers. This study aims to extract quantitative BC imaging biomarkers from CT scans to assess disease severity, define BC phenotypes, track changes over time and predict survival.Materials and methods CT exams were extracted from a prospectively maintained cohort of 452 SSc patients. 128 patients with at least one CT exam were included. An artificial intelligence-based 3D body composition analysis (BCA) algorithm assessed muscle volume, different adipose tissue compartments, and bone mineral density. These parameters were analysed with regard to various clinical, laboratory, functional parameters and survival. Phenotypes were identified performing K-means cluster analysis. Longitudinal evaluation of BCA changes employed regression analyses.Results A regression model using BCA parameters outperformed models based on Body Mass Index and clinical parameters in predicting survival (area under the curve (AUC)=0.75). Longitudinal development of the cardiac marker enabled prediction of survival with an AUC=0.82. Patients with altered BCA parameters had increased ORs for various complications, including interstitial lung disease (p<0.05). Two distinct BCA phenotypes were identified, showing significant differences in gastrointestinal disease manifestations (p<0.01).Conclusion This study highlights several parameters with the potential to reshape clinical pathways for SSc patients. Quantitative BCA biomarkers offer a means to predict survival and individual disease manifestations, in part outperforming established parameters. These insights open new avenues for research into the mechanisms driving body composition changes in SSc and for developing enhanced disease management tools, ultimately leading to more personalised and effective patient care. |
| format | Article |
| id | doaj-art-c93ceeb80b7f4a62a3c6619a3645ff32 |
| institution | Kabale University |
| issn | 2056-5933 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | RMD Open |
| spelling | doaj-art-c93ceeb80b7f4a62a3c6619a3645ff322025-08-20T03:29:58ZengBMJ Publishing GroupRMD Open2056-59332025-06-0111210.1136/rmdopen-2024-005090Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosisGabriela Riemekasten0Felix Nensa1Hanna Grasshoff2René Hosch3Malte Maria Sieren4Lennart Berkel5Jörg Barkhausen6Roman Kloeckner7Franz Wegner8Department of Rheumatology and Clinical Immunology, University Hospital Schleswig Holstein, Lübeck Campus, Lubeck, Schleswig-Holstein, GermanyInstitute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, GermanyDepartment of Rheumatology and Clinical Immunology, University Hospital Schleswig Holstein, Lübeck Campus, Lubeck, Schleswig-Holstein, GermanyInstitute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, GermanyInstitute of Radiology and Nuclear Medicine, University Hospital Schleswig Holstein, Luebeck, GermanyInstitute of Radiology and Nuclear Medicine, University Hospital Schleswig Holstein, Luebeck, GermanyInstitute of Radiology and Nuclear Medicine, University Hospital Schleswig Holstein, Luebeck, GermanyInstitute of Interventional Radiology, University Hospital Schleswig Holstein, Luebeck, GermanyInstitute of Interventional Radiology, University Hospital Schleswig Holstein, Luebeck, GermanyIntroduction Systemic sclerosis (SSc) is a complex inflammatory vasculopathy with diverse symptoms and variable disease progression. Despite its known impact on body composition (BC), clinical decision-making has yet to incorporate these biomarkers. This study aims to extract quantitative BC imaging biomarkers from CT scans to assess disease severity, define BC phenotypes, track changes over time and predict survival.Materials and methods CT exams were extracted from a prospectively maintained cohort of 452 SSc patients. 128 patients with at least one CT exam were included. An artificial intelligence-based 3D body composition analysis (BCA) algorithm assessed muscle volume, different adipose tissue compartments, and bone mineral density. These parameters were analysed with regard to various clinical, laboratory, functional parameters and survival. Phenotypes were identified performing K-means cluster analysis. Longitudinal evaluation of BCA changes employed regression analyses.Results A regression model using BCA parameters outperformed models based on Body Mass Index and clinical parameters in predicting survival (area under the curve (AUC)=0.75). Longitudinal development of the cardiac marker enabled prediction of survival with an AUC=0.82. Patients with altered BCA parameters had increased ORs for various complications, including interstitial lung disease (p<0.05). Two distinct BCA phenotypes were identified, showing significant differences in gastrointestinal disease manifestations (p<0.01).Conclusion This study highlights several parameters with the potential to reshape clinical pathways for SSc patients. Quantitative BCA biomarkers offer a means to predict survival and individual disease manifestations, in part outperforming established parameters. These insights open new avenues for research into the mechanisms driving body composition changes in SSc and for developing enhanced disease management tools, ultimately leading to more personalised and effective patient care.https://rmdopen.bmj.com/content/11/2/e005090.full |
| spellingShingle | Gabriela Riemekasten Felix Nensa Hanna Grasshoff René Hosch Malte Maria Sieren Lennart Berkel Jörg Barkhausen Roman Kloeckner Franz Wegner Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis RMD Open |
| title | Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis |
| title_full | Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis |
| title_fullStr | Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis |
| title_full_unstemmed | Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis |
| title_short | Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis |
| title_sort | computed tomography derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis |
| url | https://rmdopen.bmj.com/content/11/2/e005090.full |
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