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

作者：Zhang, Nannan; Zhang, Xinxin; Li, Junheng; Ren, Jie; Li, Luyang; Dong, Wenlei^*; Liu, Yixin^*

来源：PHYSICA MEDICA, 2023, 107: 102546.

DOI：10.1016/j.ejmp.2023.102546

• Computed tomography
• Radiomics
• Non-small cell lung cancer
• Radiotherapy
• Predictor

Background: Radiomics provides an opportunity to minimize adverse effects and optimize the efficacy of treat-ments 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 inde-pendent 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 clin-icopathological 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 thera-peutic efficacy of NSCLC patients receiving radiotherapy and demonstrate unique advantage for clinical application.

• 单位
  哈尔滨医科大学