Super-resolution and habitat radiomics based computed tomography machine-learning model for prediction of lung invasive adenocarcinoma: a multi-centre study

The spatially resolved habitat radiomics model exhibited higher discriminative accuracy than the classical intra-tumor radiomics model. The combined nomogram framework, which integrated intra-tumor radiomics, habitat radiomics, and significant clinical biomarkers, achieved state-of-the-art performance in IAC stratification. This framework provides a robust tool for precision therapeutic decision-making in pulmonary nodule management.