CT-derived radiomic analysis for predicting the survival rate of patients with non-small cell lung cancer receiving radiotherapy.

BACKGROUND

Radiomics provides an opportunity to minimize adverse effects and optimize the efficacy of treatments noninvasively. This study aims to develop a computed tomography (CT) derived radiomic signature to predict radiological response for the patients with non-small cell lung cancer (NSCLC) receiving radiotherapy.

METHODS

Total 815 NSCLC patients receiving radiotherapy were sourced from public datasets. Using CT images of 281 NSCLC patients, we adopted genetic algorithm to establish a predictive radiomic signature for radiotherapy that had optimal C-index value by Cox model. Survival analysis and receiver operating characteristic curve were performed to estimate the predictive performance of the radiomic signature. Furthermore, radiogenomics analysis was performed in a dataset with matched images and transcriptome data.

RESULTS

Radiomic signature consisting of three features was established and then validated in the validation dataset (log-rank P = 0.0047) including 140 patient, and showed a significant predictive power in two independent datasets totaling 395 NSCLC patients with binary 2-year survival endpoint. Furthermore, the novel proposed radiomic nomogram significantly improved the prognostic performance (concordance index) of clinicopathological factors. Radiogenomics analysis linked our signature with important tumor biological processes (e.g. Mismatch repair, Cell adhesion molecules and DNA replication) associated with clinical outcomes.

CONCLUSIONS

The radiomic signature, reflecting tumor biological processes, could noninvasively predict therapeutic efficacy of NSCLC patients receiving radiotherapy and demonstrate unique advantage for clinical application.