Abstract
Pulmonary tumor thrombotic microangiopathy (PTTM) is a rare, rapidly progressive malignancy-related pulmonary vascular condition that remains difficult to diagnose before death due to its nonspecific clinical and imaging features. Recent advances in metabolomics, particularly when powered by artificial intelligence (AI), offer promising opportunities for early detection by identifying distinct metabolic signatures. While AI-driven metabolomics has shown success in various cancers, its application to PTTM is challenged by the rarity of the disease, limited datasets, and privacy concerns. Innovations such as multicenter collaborations and blockchain-based data sharing may help overcome these barriers. Integrating AI and metabolomics has the potential to revolutionize not only the diagnosis of PTTM but also other rare diseases with elusive early biomarkers.