Harnessing Artificial Intelligence for Accurate Diagnosis and Radiomics Analysis of Combined Pulmonary Fibrosis and Emphysema: Insights from a Multicenter Cohort Study

Combined Pulmonary Fibrosis and Emphysema (CPFE), formally recognized as a distinct pulmonary syndrome in 2022, is characterized by unique clinical features and pathogenesis that can lead to respiratory failure and death. However, the diagnosis of CPFE presents significant challenges that hinder effective treatment. Here, we assembled a multicenter dataset of three-dimensional (3D) computed tomography (CT) images of patients' lungs from multiple hospitals across different provinces in China, including Xiangya Hospital, West China Hospital, and Fujian Provincial Hospital. Utilizing this dataset, we developed CPFENet, a deep learning-assisted diagnostic model specifically designed for CPFE diagnosis. This model accurately classifies patients into CPFE and chronic obstructive pulmonary disease (COPD), achieving diagnostic performance comparable to that of professional radiologists. Furthermore, we developed a CPFE score derived from radiomic analysis of 3D CT images, which effectively quantifies disease characteristics. Notably, female patients demonstrated significantly higher CPFE scores than males, suggesting potential sex-specific differences in CPFE. Overall, our study establishes the first diagnostic framework for CPFE, providing a diagnostic model and clinical indicators that enable accurate classification and characterization of the syndrome.