A prognostic PET radiomic model for risk stratification in non-small cell lung cancer: integrating radiogenomics and clinical features to predict survival and uncover tumor biology insights.

PURPOSE

To develop a survival risk score using F-FDG PET radiomic features for non-small cell lung cancer (NSCLC) patients and to evaluate its biological basis as a prognostic radiomic signature through radiogenomic analyses.

METHODS

We utilized several NSCLC cohort datasets from the Cancer Imaging Archive (TCIA) for radiomic analysis, where transcriptomics data were available through the Cancer Genome Atlas (TCGA). A total of 945 radiomic features were extracted from the segmented tumors. A survival-based radiomic model was developed, from which a radiomic risk score was calculated. Radiogenomic analyses were then performed to explore correlations between radiomic risk cohorts and tumor transcriptomics, oncogenic pathways, and genetic mutations. We also constructed a nomogram by combining clinical and radiomic risk factors.

RESULTS

The PET-radiomic model significantly predicted the 5-year survival rate of patients, with AUCs of 0.78, 0.71, and 0.73 in the training, validation, and testing cohorts, respectively. Integration of clinical features and the radiomic risk score in a nomogram demonstrated enhanced efficacy, achieving AUCs greater than 0.85. Radiogenomic analysis revealed that while the low-risk group indicated anti-tumor immunity, the high-risk group exhibited transcriptomic characteristics associated with enhanced tumor aggressiveness, with consistent correlations between risk group membership, oncogenic pathways, immune cell types, and critical gene alterations.

CONCLUSION

PET-radiomic features successfully delineated high- and low-risk NSCLC patient groups. Supporting radiogenomic analysis identified tumor-promoting characteristics and immune-suppressing activity in the high-risk group, which is consistent with these patients' prognoses.