Natural killer (NK) cells are crucial for tumor prognosis; however, their role in non-small-cell lung cancer (NSCLC) remains unclear. The current detection methods for NSCLC are inefficient and costly. Therefore, radiomics represent a promising alternative.
We analyzed the radiogenomics datasets to extract clinical, radiological, and transcriptome data. The effect of NK cells on the prognosis of NSCLC was assessed. Tumors were delineated using a 3D Slicer, and features were extracted using pyradiomics. A radiomics model was developed and validated using five-fold cross-validation. A nomogram model was constructed using the selected clinical variables and a radiomic score (RS). The CIBERSORTx database and gene set enrichment analysis were used to explore the correlations of NK cell infiltration and molecular mechanisms.
Higher infiltration of NK cells was correlated with better overall survival (OS) (
Our radiomic model accurately predicted NK cell infiltration in NSCLC. Combined with clinical characteristics, it can predict the prognosis of patients with NSCLC. Bioinformatic analysis revealed the gene expression and pathways underlying NK cell infiltration in NSCLC.