Artificial Intelligence Analysis of HRCT Images Reflects Pulmonary Involvement in Systemic Sclerosis Interstitial Lung Disease

Enrico De Lorenzis¹, Lucy Elizabeth Thornton², Stefano Di Donato³, Marco Minerba³, Lesley-Anne Bissell⁴, Riccardo Bixio⁵, Anand Devaraj⁶, Peter George⁷ and Francesco Del Galdo⁸, ¹Catholic University of the Sacred Heart, Roma, Rome, Italy, ²1. Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, 2. NIHR Leeds Biomedical Research Centre, United Kingdom, Leeds, ³1. Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, 2. NIHR Leeds Biomedical Research Centre, United Kingdom, Leeds, United Kingdom, ⁴Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom, ⁵University of Verona, Verona, Verona, Italy, ⁶Department of Radiology, Royal Brompton Hospital, London, London, United Kingdom, ⁷Department of Interstitial Lung Disease, Royal Brompton Hospital London, London, United Kingdom, ⁸University of Leeds, Leeds, United Kingdom

Meeting: ACR Convergence 2024

Keywords: Biomarkers, Computed tomography (CT), interstitial lung disease, pulmonary, Scleroderma, Systemic

Session Information

Date: Monday, November 18, 2024

Title: Imaging of Rheumatic Diseases Poster II

Session Type: Poster Session C

Session Time: 10:30AM-12:30PM

Background/Purpose: Interstitial lung disease (ILD) is the leading cause of mortality and morbidity in systemic sclerosis (SSc) patients. Current functional, imaging, and clinical measures of lung involvement could be biased in SSc due to its multiorgan nature, with extra-articular involvement (e.g., cardiac, musculoskeletal, esophageal) potentially affecting pulmonary function tests, high-resolution computed tomography (HRCT) findings, and respiratory symptoms. Artificial intelligence (AI) reading of HRCT has emerged as a novel tool for the objective and reliable assessment of pulmonary diseases. The aim of this study is to correlate e-Lung measures, an AI-based software for HRCT image assessment, with current standard measures of pulmonary involvement used in the routine care of SSc-ILD patients.

Methods: e-Lung software (Brainomix Ltd, UK) was applied to baseline HRCT images of consecutive SSc-ILD patients. Measures of total lung volumes (TLV), percentage of ground-glass opacities (GGO%), percentage of abnormal lung (AL%), and (weighted) reticulo-vascular score (RVS-WRVS) reflecting the severity of reticular and vascular changes were derived.

Results: A total of 68 patients’ HRCT scans were assessed by e-Lung. Clinical characteristics of the enrolled patients are reported in Table 1. Forced vital capacity (FVC) showed a moderate positive correlation with TLV (Rs=0.44, p< 0.001) and moderate negative correlations with GGO% (Rs-0.47, p< 0.001), AL% (Rs=-0.63, p< 0.001), and WRVS (Rs=-0.66, p< 0.001). Alveolar diffusion of carbon monoxide (DLCO) showed a moderate positive correlation with TLV (Rs=0.41, p=0.001), AL% (Rs=-0.46, p< 0.001), and WRVS (Rs=-0.55, p< 0.001), and a weak negative correlation with GGO% (Rs=-0.27, p=0.039). Finally, higher median WRVS values were reported in the subgroup patients with pulmonary artery systolic pressure ≥40 mmHg on heart ultrasound [8.73 (IQR 7.21-15.50) vs 7.3 (IQR 5.24-10.61), p=0.032] or with mean pulmonary artery pressure ≥25 mmHg on right heart catheterization [14.01 (IQR 7.21-19.19) vs 7.48 (IQR 5.36-10.22), p=0.012] compared to the rest of the patients.

Conclusion: e-Lung assessment was feasible in SSc-ILD patients and provided AI-derived measures from HRCT images that correlated with FVC and DLCO. WRVS was also higher in patients with pulmonary hypertension. AI could integrate current measures of assessment for pulmonary complications in SSc patients.

Table 1: Characteristics of the patients

Disclosures: E. De Lorenzis: None; L. Thornton: None; S. Di Donato: None; M. Minerba: None; L. Bissell: AbbVie, 6, Alfasigma, 6, Galapagos, 6, UCB, 6; R. Bixio: Accord Healthcare, 6, GlaxoSmithKlein(GSK), 6; A. Devaraj: AstraZeneca, 2, Boehringer-Ingelheim, 2, Brainomix, 2, 11, Genentech, 2, Roche, 2, Vicore, 2; P. George: AstraZeneca, 2, Avalyn, 2, Boehringer-Ingelheim, 2, Brainomix, 11, 12, Senior Medical Director, Daiichi-Sankyo, 2, GlaxoSmithKlein(GSK), 2, Roche, 2; F. Del Galdo: AbbVie, 2, 5, Argenx, 2, Arxx, 2, 5, AstraZeneca, 2, 5, Boehringer Ingelheim, 2, 5, Chemomab, 5, Deepcure, 2, GlaxoSmithKline (GSK), 2, Janssen, 2, Mitsubishi Tanabe, 5, Novartis, 2, Ventus, 2.

To cite this abstract in AMA style:

De Lorenzis E, Thornton L, Di Donato S, Minerba M, Bissell L, Bixio R, Devaraj A, George P, Del Galdo F. Artificial Intelligence Analysis of HRCT Images Reflects Pulmonary Involvement in Systemic Sclerosis Interstitial Lung Disease [abstract]. Arthritis Rheumatol. 2024; 76 (suppl 9). https://acrabstracts.org/abstract/artificial-intelligence-analysis-of-hrct-images-reflects-pulmonary-involvement-in-systemic-sclerosis-interstitial-lung-disease/. Accessed .

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