Identifying 18F-FDG PET-metabolic radiomic signature for lung adenocarcinoma prognosis via the leveraging of prognostic transcriptomic module

BackgroundImaging with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET), which identifies molecular and metabolic abnormalities within tumor cells, could support prognostic assessment of lung adenocarcinoma (LUAD). We aimed to develop a radiomic signature with the aid of a transcriptomic module for individualized clinical prognostic assessment of LUAD patients. MethodsUsing a gene expression profile consisting of 334 stage I-IIIA LUAD patients, prognostic-related gene coexpression modules were constructed via a weighted correlation network analysis algorithm. The robustness and prognostic performance of the coexpression modules were then tested across 2 gene expression datasets totaling 331 patients. Finally, using a discovery dataset with matched transcriptomic and 18F-FDG PET radiomic data of 15 patients and multiple linear regression analysis, we developed a PET-metabolic radiomic signature that had optimal correlation with the expression of a robust prognostic module. ResultsWe selected a superior coexpression module for LUAD prognosis in which the genes were significantly enriched in important biological processes associated with tumors (e.g., cell cycle, DNA replication and p53 signaling pathway). The prognostic performance of the module for overall survival (OS) and recurrence-free survival (RFS) was validated in 2 independent gene expression datasets (log-rank P