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

Background: Early differentiation between invasive adenocarcinoma (IAC) and non-IAC pulmonary nodules is crucial for guiding clinical decision-making. Therefore, this study aimed to distinguish IAC from non-IAC pulmonary nodules using intra-tumor radiomics signatures, habitat radiomics analysis, and a combined nomogram by integrating generative adversarial network (GAN) based super-resolution reconstruction. Methods: In this multi-center retrospective study, 858 patients [mean ± standard deviation (SD): 57.635±12.978 years] were enrolled as the training set (Center 1, 501 non-IAC cases Results: The Habitat-Model outperformed Intra-Model (training AUC: 0.893 Conclusions: 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.