Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients

Abstract

Abstract Background Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapidly evolving interstitial lung disease (ILD), driving its mortality. Specific imaging-based biomarkers associated with the evolution of lung disease are needed to help predict and quantify ILD. Methods We evaluated the potential of an automated ILD quantification system (icolung®) from chest CT scans, to help in quantification and prediction of ILD progression in SSc-ILD. We used a retrospective cohort of 75 SSc-ILD patients to evaluate the potential of the AI-based quantification tool and to correlate image-based quantification with pulmonary function tests and their evolution over time. Results We evaluated a group of 75 patients suffering from SSc-ILD, either limited or diffuse, of whom 30 presented progressive pulmonary fibrosis (PPF). The patients presenting PPF exhibited more extensive lesions (in % of total lung volume (TLV)) based on image analysis than those without PPF: 3.93 (0.36–8.12)* vs. 0.59 (0.09–3.53) respectively, whereas pulmonary functional test showed a reduction in Force Vital Capacity (FVC)(pred%) in patients with PPF compared to the others : 77 ± 20% vs. 87 ± 19% (p < 0.05). Modifications of FVC and diffusing capacity of the lungs for carbon monoxide (DLCO) over time were correlated with longitudinal radiological ILD modifications (r=-0.40, p < 0.01; r=-0.40, p < 0.01 respectively). Conclusion AI-based automatic quantification of lesions from chest-CT images in SSc-ILD is correlated with physiological parameters and can help in disease evaluation. Further clinical multicentric validation is necessary in order to confirm its potential in the prediction of patient’s outcome and in treatment management.

Keywords

• Systemic sclerosis
• Interstitial lung disease
• Artificial intelligence
• Computed tomography
• Pulmonary function tests