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...
Saved in:
| Main Authors: | Gabriela Riemekasten, Felix Nensa, Hanna Grasshoff, René Hosch, Malte Maria Sieren, Lennart Berkel, Jörg Barkhausen, Roman Kloeckner, Franz Wegner |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2025-06-01
|
| Series: | RMD Open |
| Online Access: | https://rmdopen.bmj.com/content/11/2/e005090.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myeloma
by: Franz Wegner, et al.
Published: (2025-07-01) -
Impact of Radiologist Experience on AI Annotation Quality in Chest Radiographs: A Comparative Analysis
by: Malte Michel Multusch, et al.
Published: (2025-03-01) -
Combination therapy of rituximab and mycophenolate in patients with systemic sclerosis and primary cardiac involvement refractory to cyclophosphamide: a retrospective exploratory analysis of 10 cases
by: Gabriela Riemekasten, et al.
Published: (2025-06-01) -
External phantom-based validation of a deep-learning network trained for upscaling of digital low count PET data
by: Anja Braune, et al.
Published: (2025-04-01) -
Psychiatric manifestations in multiple sclerosis patients and multiple sclerosis in psychiatric patient
by: Yára Dadalti Fragoso, et al.
Published: (2009-12-01)