Proceedings of the American College of Rheumatology/Association of Physicians of Great Britain and Ireland Connective Tissue Disease-Associated Interstitial Lung Disease Summit: A Multidisciplinary Approach to Address Challenges and Opportunities

Early detection of interstitial lung disease in rheumatic diseases: A joint statement from the Portuguese Pulmonology Society, the Portuguese Rheumatology Society, and the Portuguese Radiology and Nuclear Medicine Society

INTRODUCTION

Interstitial lung disease (ILD) contributes significantly to morbidity and mortality in connective tissue disease (CTD). Early detection and accurate diagnosis are essential for informing treatment decisions and prognosis in this setting. Clear guidance on CTD-ILD screening, however, is lacking.

OBJECTIVE

To establish recommendations for CTD-ILD screening based on the current evidence.

METHOD

Following an extensive literature research and evaluation of articles selected for their recency and relevance to the characterization, screening, and management of CTD-ILD, an expert panel formed by six pulmonologists from the Portuguese Society of Pulmonology, six rheumatologists from the Portuguese Society of Rheumatology, and six radiologists from the Portuguese Society of Radiology and Nuclear Medicine participated in a multidisciplinary discussion to produce a joint statement on screening recommendations for ILD in CTD.

RESULTS

The expert panel achieved consensus on when and how to screen for ILD in patients with systemic sclerosis, rheumatoid arthritis, mixed connective tissue disease, Sjögren syndrome, idiopathic inflammatory myopathies and systemic lupus erythematous.

CONCLUSIONS

Despite the lack of data on screening for CTD-ILD, an expert panel of pulmonologists, rheumatologists and radiologists agreed on a series of screening recommendations to support decision-making and enable early diagnosis of ILD to ultimately improve outcomes and prognosis in patients with CTD.

Understanding Interstitial Lung Diseases Associated with Connective Tissue Disease (CTD-ILD): Genetics, Cellular Pathophysiology, and Biologic Drivers

Connective tissue disease-associated interstitial lung disease (CTD-ILD) is a collection of systemic autoimmune disorders resulting in lung interstitial abnormalities or lung fibrosis. CTD-ILD pathogenesis is not well characterized because of disease heterogeneity and lack of pre-clinical models. Some common risk factors are inter-related with idiopathic pulmonary fibrosis, an extensively studied fibrotic lung disease, which includes genetic abnormalities and environmental risk factors. The primary pathogenic mechanism is that these risk factors promote alveolar type II cell dysfunction triggering many downstream profibrotic pathways, including inflammatory cascades, leading to lung fibroblast proliferation and activation, causing abnormal lung remodeling and repairs that result in interstitial pathology and lung fibrosis. In CTD-ILD, dysregulation of regulator pathways in inflammation is a primary culprit. However, confirmatory studies are required. Understanding these pathogenetic mechanisms is necessary for developing and tailoring more targeted therapy and provides newly discovered disease biomarkers for early diagnosis, clinical monitoring, and disease prognostication. This review highlights the central CTD-ILD pathogenesis and biological drivers that facilitate the discovery of disease biomarkers.

Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations

BACKGROUND

The need for quantitative assessment of interstitial lung involvement on thin-section computed tomography (CT) has arisen in interstitial lung diseases including connective tissue disease (CTD).

PURPOSE

To evaluate the capability of machine learning (ML)-based CT texture analysis for disease severity and treatment response assessments in comparison with qualitatively assessed thin-section CT for patients with CTD.

MATERIAL AND METHODS

A total of 149 patients with CTD-related ILD (CTD-ILD) underwent initial and follow-up CT scans (total 364 paired serial CT examinations), pulmonary function tests, and serum KL-6 level tests. Based on all follow-up examination results, all paired serial CT examinations were assessed as "Stable" (n = 188), "Worse" (n = 98) and "Improved" (n = 78). Next, quantitative index changes were determined by software, and qualitative disease severity scores were assessed by consensus of two radiologists. To evaluate differences in each quantitative index as well as in disease severity score between paired serial CT examinations, Tukey's honestly significant difference (HSD) test was performed among the three statuses. Stepwise regression analyses were performed to determine changes in each pulmonary functional parameter and all quantitative indexes between paired serial CT scans.

RESULTS

Δ% normal lung, Δ% consolidation, Δ% ground glass opacity, Δ% reticulation, and Δdisease severity score showed significant differences among the three statuses (P < 0.05). All differences in pulmonary functional parameters were significantly affected by Δ% normal lung, Δ% reticulation, and Δ% honeycomb (0.16 ≤r² ≤0.42; P < 0.05).

CONCLUSION

ML-based CT texture analysis has better potential than qualitatively assessed thin-section CT for disease severity assessment and treatment response evaluation for CTD-ILD.

Rheumatological evaluation of patients with interstitial lung disease

Objective: Interstitial lung disease (ILD) is a common feature of connective tissue disease (CTD). The diagnosis of CTD-ILD can be challenging and is important for therapeutic decisions. In this study, we aimed to determine whether a systematic rheumatological assessment could help pulmonologists in the diagnosis and care of ILD patients.Method: We conducted an observational single-centre study of patients with ILD. All patients underwent standardized pulmonary and rheumatological evaluations, including clinical evaluation (pulmonary symptoms and musculoskeletal signs), immunological screening, chest high-resolution computed tomography, pulmonary function tests, and ultrasonography (US) of joints and major salivary glands.Results: We included 100 consecutive ILD patients (47% women, mean ± sd age 67 ± 14 years); 15 patients already had CTD. The main extrapulmonary symptoms were joint pain (n = 52), joint swelling (n = 26), and sicca syndrome (n = 33). US of joints revealed synovitis, bone erosion, and tenosynovitis in 37, 17, and 13 patients, respectively. US of major salivary glands detected features associated with Sjögren's syndrome in 13 patients. After rheumatological evaluation, CTD-ILD was confidently diagnosed in 39 patients; diseases were mainly rheumatoid arthritis (n = 20), primary Sjögren's syndrome (n = 17), and inflammatory myopathies (n = 7). The diagnosis of CTD-ILD was associated with the presence of musculoskeletal symptoms and immunological and US abnormalities. The CTD diagnosis led to a therapeutic change in 21 patients.Conclusion: Our findings suggest that musculoskeletal symptoms are frequent in ILD patients, which supports multidisciplinary management, involving the rheumatologist, for evaluating patients with ILD.

Machine learning for lung CT texture analysis: Improvement of inter-observer agreement for radiological finding classification in patients with pulmonary diseases

PURPOSE

To evaluate the capability ML-based CT texture analysis for improving interobserver agreement and accuracy of radiological finding assessment in patients with COPD, interstitial lung diseases or infectious diseases.

MATERIALS AND METHODS

Training cases (n = 28), validation cases (n = 17) and test cases (n = 89) who underwent thin-section CT at a 320-detector row CT with wide volume scan and two 64-detector row CTs with helical scan were enrolled in this study. From 89 CT data, a total of 350 computationally selected ROI including normal lung, emphysema, nodular lesion, ground-glass opacity, reticulation and honeycomb were evaluated by three radiologists as well as by the software. Inter-observer agreements between consensus reading with and without using the software or software alone and standard references determined by consensus of pulmonologists and chest radiologists were determined using κ statistics. Overall distinguishing accuracies were compared among all methods by McNemar's test.

RESULTS

Agreements for consensus readings obtained with and without the software or the software alone with standard references were determined as significant and substantial or excellent (with the software: κ = 0.91, p < 0.0001; without the software: κ = 0.81, p < 0.0001; the software alone: κ = 0.79, p < 0.0001). Overall differentiation accuracy of consensus reading using the software (94.9 [332/350] %) was significantly higher than that of consensus reading without using the software (84.3 [295/350] %, p < 0.0001) and the software alone (82.3 [288/350] %, p < 0.0001).

CONCLUSION

ML-based CT texture analysis software has potential for improving interobserver agreement and accuracy for radiological finding assessments in patients with COPD, interstitial lung diseases or infectious diseases.