Biomarkers have been actively investigated to supplement functional and imaging modalities to predict the severity, therapeutic responsiveness, and progression of connective tissue disease-associated interstitial lung disease (CTD-ILD). This study aimed to evaluate Krebs von den Lungen 6 (KL-6) as a potential biomarker reflecting the severity of CTD-ILD as assessed through computed tomography (CT) and pulmonary function test (PFT) parameters. This retrospective study included 549 Korean patients with rheumatoid arthritis, systemic sclerosis, inflammatory myositis, and other CTDs with or without concurrent ILD. Serum KL-6 concentration (U/mL) was measured using the latex-enhanced immunoturbidimetric assay method. CT and PFT results were collected within 1 year of serum collection. A semiquantitative grade of ILD extent was evaluated through CT scan (grade 1, 0–25%; grade 2, 26–50%; grade 3, 51–75%; grade 4, 76–100%). CTD-ILD patients (n = 165) had elevated serum KL-6 levels compared to CTD patients without ILD (n = 384) (p < 0.001), and those findings were preserved after adjusting for age, sex, and CTD type. The semiquantitative grade of ILD on CT scan was significantly proportional to the KL-6 level, and the optimal cut-off KL-6 value effectively differentiated each ILD grade. The percent diffusing capacity of the lung for carbon monoxide (DLCO) (p < 0.001) and forced vital capacity (FVC) (p < 0.001) parameters had a moderate, negative correlation with the KL-6 level. Serum KL-6 levels were increased in CTD-ILD patients and had a positive correlation with CT grade and a negative correlation with FVC and DLCO. Serum KL-6 levels may reflect CTD-ILD severity.

Objective: The assessment of disease activity in Takayasu arteritis (TA) is difficult in clinical situations because clinical symptoms and laboratory parameters do not always reflect the actual inflammation of the arterial wall. We undertook this study to comprehensively investigate the role of ¹⁸F‐fluorodeoxyglucose–positron emission tomography (FDG‐PET) in the assessment of disease activity in patients with TA.Methods: We performed a retrospective chart review of 53 FDG‐PET scans in 38 patients with TA. We measured ¹⁸F‐fluorodeoxyglucose (¹⁸F‐FDG) accumulation in the vascular wall of the large vessel using semiquantitative (visual grade) and quantitative (standard uptake value intensity) analyses. Clinical disease activity was evaluated based on the National Institutes of Health criteria for active TA, and erythrocyte sedimentation rates (ESRs) and C‐reactive protein (CRP) levels were measured.Results: At baseline, active vascular ¹⁸F‐FDG uptake (visual grade ≥2) was observed in 18 of 24 patients with active disease and in 5 of 14 patients with inactive disease. There was a significant association between clinical disease activity and disease activity judged by FDG‐PET (P = 0.008). Visual grade, standard uptake value intensity, and the number of vascular lesions with active ¹⁸F‐FDG uptake were significantly higher in patients with active disease and correlated well with the ESR and CRP levels. In 15 followup FDG‐PET scans, the changes in visual grade, areas of active vascular ¹⁸F‐FDG uptake, and standard uptake value intensity reflected changes in clinical disease activity.Conclusion: ¹⁸F‐FDG uptake was associated with clinical disease activity and markers of inflammation, and FDG‐PET reflected changes in clinical disease activity in patients with TA. FDG‐PET may be a useful tool for aiding in the assessment of disease activity in patients with TA.

Ha YJ, et al. J Korean Med Sci. 2014 Sep;29(9):1199-1204. http://dx.doi.org/10.3346/jkms.2014.29.9.1199

Autoantibodies encountered in patients with systemic rheumatic diseases bear clinical significance as a biomarker to help or predict diagnosis, clinical phenotypes, prognosis, and treatment decision-making. Furthermore, evidence has accumulated regarding the active involvement of disease-specific or disease-associated autoantibodies in the pathogenic process beyond simple association with the disease, and such knowledge has become essential for us to better understand the clinical value of autoantibodies as a biomarker. This review will focus on the current update on the autoantibodies of four rheumatic diseases (rheumatoid arthritis, myositis, systemic sclerosis, and anti-neutrophil cytoplasmic antibody associated vasculitis) where there has been a tremendous progress in our understanding on their biological effects and clinical use.

Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the sacroiliac joints. In this study, we develop a method for detecting bone marrow edema by magnetic resonance (MR) imaging of the sacroiliac joints and a deep-learning network. A total of 815 MR images of the sacroiliac joints were obtained from 60 patients diagnosed with axSpA and 19 healthy subjects. Gadolinium-enhanced fat-suppressed T1-weighted oblique coronal images were used for deep learning. Active sacroiliitis was defined as bone marrow edema, and the following processes were performed: setting the region of interest (ROI) and normalizing it to a size suitable for input to a deep-learning network, determining bone marrow edema using a convolutional-neural-network-based deep-learning network for individual MR images, and determining sacroiliac arthritis in subject examinations based on the classification results of individual MR images. About 70% of the patients and normal subjects were randomly selected for the training dataset, and the remaining 30% formed the test dataset. This process was repeated five times to calculate the average classification rate of the five-fold sets. The gradient-weighted class activation mapping method was used to validate the classification results. In the performance analysis of the ResNet18-based classification network for individual MR images, use of the ROI showed excellent detection performance of bone marrow edema with 93.55 ± 2.19% accuracy, 92.87 ± 1.27% recall, and 94.69 ± 3.03% precision. The overall performance was additionally improved using a median filter to reflect the context information. Finally, active sacroiliitis was diagnosed in individual subjects with 96.06 ± 2.83% accuracy, 100% recall, and 94.84 ± 3.73% precision. This is a pilot study to diagnose bone marrow edema by deep learning based on MR images, and the results suggest that MR analysis using deep learning can be a useful complementary means for clinicians to diagnose bone marrow edema.