Super-resolution and habitat radiomics based computed tomography machine-learning model for prediction of lung invasive adenocarcinoma: a multi-centre study
Year: 2026
Type: article
Study design: Observational Study
Abstract: 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: 89 IAC cases; mean ± SD: 57.037±11.683 years) were included. Univariate and multivariate analyses were conducted to explore clinical characteristics. Radiomics features were extracted from intra-tumor regions and sub-regions. After feature selection, machine learning models, namely the Intra-Model and Habitat-Model, were developed. A combined nomogram integrating significant clinical factors, intra-tumor radiomics and habitat radiomics was constructed and evaluated using area under receiver operator characteristics curve (AUC), decision curve analysis (DCA), and other quantified metrics. Results: 0.875) in predicting IAC invasiveness. The combined nomogram demonstrated an incremental advancement in IAC stratification [training AUC: 0.907 (95% CI: 0.887-0.927); testing AUC: 0.895 (95% CI: 0.849-0.941)], with DCA confirming 28-34% net benefit improvement over single-modality approaches at critical thresholds (10-25% risk). Age (P<0.001) and nodule diameter (P<0.001), along with intra-tumor and habitat radiomics, were identified as key contributing factors. 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.
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7128700455