Computed tomography of interstitial lung disease in systemic sclerosis: dataset and deep learning model for pulmonary lesion segmentation

OBJECTIVE: Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is a major cause of morbidity and mortality in systemic sclerosis patients. High-resolution computed tomography (HRCT) plays a crucial role in SSc-ILD diagnosis and management. In this study, we aimed to develop and evaluate a deep learning-based automated segmentation model for quantifying SSc-ILD lesions in HRCT images and assess its clinical relevance. METHODS: We developed a convolutional neural network model to segment normal lung, established fibrosis (EF), and ground-glass opacity (GGO) in HRCT scans from 40 SSc-ILD patients. The model was trained and evaluated using 8-fold cross-validation. Segmentation performance was assessed using the Dice similarity coefficient (DSC). Correlations between predicted lesion volumes and pulmonary function test (PFT) metrics were analyzed using Spearman's ρ. RESULTS: Our model achieved a total lesion (EF+GGO) DSC of 78%. Class-wise segmentation performance was lower for EF (DSC: 70%) compared to GGO (DSC: 73%). Predicted lesion volumes showed significant negative correlations with forced expiratory volume in one second (FEV1) (ρ=-0.64, p