Multi-Omics Data-Derived Inflammatory Phenotype for ABPA Recurrence Risk Prediction: A Multicenter Study

This project aims to develop an inflammatory phenotype-based risk prediction model for recurrence of allergic bronchopulmonary aspergillosis (ABPA) to enable stratified patient management. The study integrates multidimensional data sources, including radiomics, mycobiomics, inflammatory biomarkers, pulmonary function parameters, and routine clinical records. Deep machine learning algorithms are employed to extract and select key features from these multi-omics and clinical datasets, define inflammatory phenotypes, and subsequently construct a recurrence risk prediction model. Based on the risk stratification derived from the model, low-risk individuals will receive regular follow-up, whereas high-risk individuals will undergo intensified intervention and management. This approach is expected to optimize individualized treatment strategies for ABPA patients, reduce recurrence rates, and improve clinical outcomes.