Arthritis Care Res (Hoboken)
December 2024
Objective: This studied investigated whether changes in circulating biomarkers predict progressive pulmonary fibrosis (PFF) in patients with systemic sclerosis-associated interstitial lung disease (ILD) receiving treatment.
Method: Participants of Scleroderma Lung Study (SLS) II, which compared mycophenolate (MMF) versus cyclophosphamide (CYC) for SSc-ILD, who had blood samples at baseline and 12-months were included. Levels for C-reactive protein (CRP), interleukin (IL)-6, chemokine ligand 4 (CXCL4), chemokine ligand 18 (CCL18) and Krebs von den Lungen 6 (KL-6) were measured, and a logistic regression model evaluated relationships between changes in these biomarkers and the development of PPF by 24 months.
Background: Lung computed tomography (CT) scan image registration is being used for lung function analysis such as ventilation. Given the high sensitivity of functional analyses to image registration errors, an image registration error scoring tool that can measure submillimeter image registration errors is needed.
Purpose: To propose an image registration error scoring tool, termed λ, whose spatial sensitivity can be used to quantify image registration errors in steep image gradient regions under realistic noise conditions.
Objectives: To explore the association between the extent of CT abnormalities by quantitative imaging analysis (QIA) and clinical/physiological disease parameters in patients with antisynthetase syndrome associated interstitial lung disease (ARS-ILD).
Methods: We analysed 20 patients with antisynthetase antibodies and active ILD enrolled in the Abatacept in Myositis-Associated Interstitial Lung Disease study. High-resolution chest CT was obtained at weeks 0, 24 and 48 and QIA scored the extent of ground glass (quantitative score for ground glass), fibrosis (quantitative score for lung fibrosis, QLF) and total ILD (quantitative ILD, QILD).
The Response Evaluation in Solid Tumors (RECIST) 1.1 provides key guidance for performing imaging response assessment and defines image-based outcome metrics in oncology clinical trials, including progression free survival. In this framework, tumors identified on imaging are designated as either target lesions, non-target disease or new lesions and a structured categorical response is assigned at each imaging time point.
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