The Extent and Diverse Trajectories of Longitudinal Changes in Rheumatoid Arthritis Interstitial Lung Diseases Using Quantitative HRCT Scores

Abatacept in usual and in non-specific interstitial pneumonia associated with rheumatoid arthritis

OBJECTIVE

To compare the effectiveness of abatacept (ABA) in Rheumatoid Arthritis-associated Interstitial Lung Disease (RA-ILD) according to the radiological patterns of usual (UIP) or non-specific interstitial pneumonia (NSIP).

METHODS

From an observational longitudinal multicentre study of 263 RA-ILD patients treated with ABA, those with UIP or NSIP were selected. Lung function, chest high resolution computerised tomography (HRCT) and dyspnoea were recorded and compared in both groups from baseline to the end of follow-up (progression definitions: improvement or worsening >10% of FVC or DLCO, changes in HRCT extension and 1-point change in the mMRC scale, respectively). Differences between final and baseline visits were calculated as the average difference (95% CI) through mixed effects models regression.

RESULTS

We studied 190 patients with UIP (n=106) and NSIP (n=84). General features were similar in both groups except for older age, positive rheumatoid factor, and previous sulfasalazine therapy, which were more frequent in patients with UIP. ILD duration up to ABA initiation was relatively short: median 16 [4-50] and 11 [2-36] months (p=0.36) in UIP and NSIP, respectively. Mean baseline FVC and DLCO were 82% and 63% in UIP and 89% and 65% in NSIP, respectively. Both parameters remained stable during 24 months with ABA. HRCT lesions and dyspnoea improved/stabilized in 73.1% and 90.5% and 72.9% and 94.6% of UIP and NSIP patterns, respectively.

CONCLUSION

ABA seems equally effective in stabilizing dyspnoea, lung function and radiological impairment in both UIP and NSIP patterns of RA-ILD. Early administration of ABA may prevent RA-ILD progression, regardless of the radiological pattern.

Quantitative interstitial lung disease scores in idiopathic inflammatory myopathies: longitudinal changes and clinical implications

OBJECTIVES

To investigate computer-aided quantitative scores from high-resolution CT (HRCT) images and determine their longitudinal changes and clinical significance in patients with idiopathic inflammatory myopathies (IIMs)-related interstitial lung disease (IIMs-ILD).

METHODS

The clinical data and HRCT images of 80 patients with IIMs who underwent serial HRCT scans at least twice were retrospectively analysed. Quantitative ILD (QILD) scores (%) were calculated as the sum of the extent of lung fibrosis, ground-glass opacity, and honeycombing. The individual time-estimated ΔQILD between two consecutive scans was derived using a linear approximation of yearly changes.

RESULTS

The baseline median QILD (interquartile range) scores in the whole lung were 28.1% (19.1-43.8). The QILD was significantly correlated with forced vital capacity (r = -0.349, P = 0.002) and diffusing capacity for carbon monoxide (r = -0.381, P = 0.001). For ΔQILD between the first two scans, according to the visual ILD subtype, QILD aggravation was more frequent in patients with usual interstitial pneumonia (UIP) than non-UIP (80.0% vs 44.4%, P = 0.013). Multivariable logistic regression analyses identified UIP was significantly related to radiographic ILD progression (ΔQILD >2%, P = 0.015). Patients with higher baseline QILD scores (>28.1%) had a higher risk of lung transplantation or death (P = 0.015). In the analysis of three serial HRCT scans (n = 41), dynamic ΔQILD with four distinct patterns (improving, worsening, convex and concave) was observed.

CONCLUSION

QILD changes in IIMs-ILD were dynamic, and baseline UIP patterns seemed to be related to a longitudinal progression in QILD. These may be potential imaging biomarkers for lung function, changes in ILD severity and prognosis in IIMs-ILD.

The potential role of artificial intelligence in the clinical practice of interstitial lung disease

Artificial intelligence (AI) is being widely applied in the field of medicine, in areas such as drug discovery, diagnostic support, and assistance with medical practice. Among these, medical imaging is an area where AI is expected to make a significant contribution. In Japan, as of November 2022, 23 AI medical devices have received regulatory approval; all these devices are related to image analysis. In interstitial lung diseases, technologies have been developed that use AI to analyze high-resolution computed tomography and pathological images, and gene expression patterns in tissue taken from transbronchial lung biopsies to assist in the diagnosis of idiopathic pulmonary fibrosis. Some of these technologies are already being used in clinical practice in the United States. AI is expected to reduce the burden on physicians, improve reproducibility, and advance personalized medicine. Obtaining sufficient data for diseases with a small number of patients is difficult. Additionally, certain issues must be addressed in order for AI to be applied in healthcare. These issues include taking responsibility for the AI results output, updating software after the launch of technology, and adapting to new imaging technologies. Establishing research infrastructures such as large-scale databases and common platforms is important for the development of AI technology: their use requires an understanding of the characteristics and limitations of the systems. CLINICAL TRIAL REGISTRATION: Not applicable.

Machine learning in radiology: the new frontier in interstitial lung diseases

Challenges for the effective management of interstitial lung diseases (ILDs) include difficulties with the early detection of disease, accurate prognostication with baseline data, and accurate and precise response to therapy. The purpose of this Review is to describe the clinical and research gaps in the diagnosis and prognosis of ILD, and how machine learning can be applied to image biomarker research to close these gaps. Machine-learning algorithms can identify ILD in at-risk populations, predict the extent of lung fibrosis, correlate radiological abnormalities with lung function decline, and be used as endpoints in treatment trials, exemplifying how this technology can be used in care for people with ILD. Advances in image processing and analysis provide further opportunities to use machine learning that incorporates deep-learning-based image analysis and radiomics. Collaboration and consistency are required to develop optimal algorithms, and candidate radiological biomarkers should be validated against appropriate predictors of disease outcomes.

Management of Connective Tissue Disease-related Interstitial Lung Disease

Purpose of Review

This review aims to collate current evidence on the screening, diagnosis, and treatment of various connective tissue disease (CTD)–associated interstitial lung diseases (CTD-ILD) and present a contemporary framework for the management of such patients. It also seeks to summarize treatment outcomes including efficacy and safety of immunosuppressants, anti-fibrotics, and stem cell transplantation in CTD-ILD.

Recent Findings

Screening for ILD has been augmented by the use of artificial intelligence, ultra-low dose computerized tomography (CT) of the chest, and the use of chest ultrasound. Serum biomarkers have not found their way into clinical practice as yet. Identifying patients who need treatment and choosing the appropriate therapy is important to minimize the risk of therapy-related toxicity. The first-line drugs for systemic sclerosis (SSc) ILD include mycophenolate and cyclophosphamide. Nintedanib, an anti-fibrotic tyrosine kinase inhibitor, is approved for use in SSc-ILD. The US Food and Drug Administration (FDA) has recently approved tocilizumab subcutaneous injection for slowing the rate of decline in pulmonary function in adult patients with SSc-ILD. Autologous stem cell transplantation may have a role in select cases of SSc-ILD.

Summary

CTD-ILD is a challenging area with diverse entities and variable outcomes. High-resolution CT is the investigative modality of choice. Treatment decisions need to be individualized and are based on patient symptoms, lung function, radiologic abnormalities, and the risk of disease progression. Precision medicine may play an important role in determining the optimal therapy for an individual patient in the future.