Stacking learning based on micro-CT radiomics for outcome prediction in the early-stage of silica-induced pulmonary fibrosis model

Abstract

Silicosis is a progressive pulmonary fibrosis disease caused by long-term inhalation of silica. The early diagnosis and timely implementation of intervention measures are crucial in preventing silicosis deterioration further. However, the lack of screening and diagnostic measures for early-stage silicosis remains a significant challenge. In this study, silicosis models of varying severity were established through a single exposure to silica with different doses (2.5mg/mice or 5mg/mice) and durations (4 weeks or 12 weeks). The diagnostic performance of computed tomography (CT) quantitative analysis was assessed using lung density biomarkers and the lung density distribution histogram, with a particular focus on non-aerated lung volume. Subsequently, we developed and evaluated a stacking learning model for early diagnosis of silicosis after extracting and selecting features from CT images. The CT quantitative analysis reveals that while the lung densitometric biomarkers and lung density distribution histogram, as traditional indicators, effectively differentiate severe fibrosis models, they are unable to distinguish early-stage silicosis. Furthermore, these findings remained consistent even when employing non-aerated areas, which is a more sensitive indicator. By establishing a radiomics stacking learning model based on non-aerated areas, we can achieve remarkable diagnostic performance to distinguish early-stage silicosis, which can provide a valuable tool for clinical assistant diagnosis. This study reveals the potential of using non-aerated lung areas as a region of interest in stacking learning for early diagnosis of silicosis, providing new insights into early detection of this disease.