Integrating computational pathology and multi-transcriptomics to characterize lung adenocarcinoma heterogeneity and prognostic modeling

This study provides a comprehensive multi-dimensional framework integrating computational pathology and single-cell multi-omics to characterize LUAD heterogeneity. By identifying CNV-associated imaging features and key molecular regulators, we propose potential biomarkers for prognosis and therapeutic targeting in LUAD. However, as this study is based primarily on retrospective bioinformatics analysis, the clinical utility of these findings requires further validation through prospective cohorts and experimental studies. These results lay the groundwork for future translational applications but should be interpreted with caution in the absence of functional validation.