Parallel comparison and combining effect of radiomic and emerging genomic data for prognostic stratification of <scp>non‐small cell</scp> lung carcinoma patients

Background A single institution retrospective analysis of 124 non‐small cell lung carcinoma (NSCLC) patients was performed to identify whether disease‐free survival (DFS) achieves incremental values when radiomic and genomic data are combined with clinical information. Methods Using the least absolute shrinkage and selection operator (LASSO) Cox regression method, radiomic and genetic features were reduced in number for selection of the most useful prognostic feature. We created four models using only baseline clinical data, clinical data with selected genetic features, clinical data with selected radiomic features, and clinical data with selected genetic and radiomic features together. Multivariate Cox proportional hazards analysis was performed to determine predictors of DFS. Receiver operating characteristic (ROC) calculation was made to compare the discriminative performance for DFS prediction by four constructed models at the five‐year time point. Results On precontrast scan, improved discrimination performance was obtained in a merging of selected radiomics and genetics (AUC = 0.8638), compared with clinical data only (AUC = 0.7990), selected genetic features (AUC = 0.8497), and selected radiomic features (AUC = 0.8355). On post‐contrast scan, discrimination performance was improved (AUC = 0.8672) compared with the clinical variables (AUC = 0.7913), and selected genetic features (AUC = 0.8376) and selected radiomic features (AUC = 0.8399) were considered. Conclusions The combination of selected radiomic and genomic features improved stratification of NSCLC patients upon survival. Thus, integrating clinicopathologic model with radiomic and genomic features may lead to improved prognostic accuracy compared to conventional clinicopathological data alone. Key points Significant findings of the study Receiver operating characteristic (ROC) calculation was made to compare the discriminative performance for disease‐free survival (DFS). The discriminative performance for DFS was better when combining radiomic and genetic features compared to clinical data only, selected genetic features, and selected radiomic features. What this study adds The combination of selected radiomic and genomic features improved stratification of NSCLC patients upon survival. Thus, integrating a clinicopathological model with radiomic and genomic features may lead to improved prognostic accuracy compared to conventional clinicopathological data alone.
We tried to compare the results of disease‐free survival (DFS) in patients with non‐small‐cell lung carcinoma (NSCLC) through radiomics with those of traditional staging systems or genetic analysis to determine if incremental values can be obtained when combining them. The addition of selected radiomics and genomic features using the LASSO method improved the stratification of lung cancer patients upon survival. Our results show that integration of radiomics and genomic features with the current clinicopathologic model may lead to improved prognostic accuracy compared to conventional clinicopathological data alone.