Identification and validation of a radiomic signature for predicting survival outcomes in non-small-cell lung cancer treated with radiation therapy

Abstract

Radiomics is a novel tool which extracts quantitative features from medical imaging, and combines key features into an image-based radiomic signature for cancer diagnostics. We aimed to develop a quantitative radiomic signature for predicting survival outcomes in non-small-cell lung cancer (NSCLC) patients treated with radiation therapy. Based on computed tomography (CT) imaging of NSCLC, we applied a forward selection procedure for the establishment of a radiomic signature in a cohort with 107 NSCLC patients treated with radiation therapy, and validated it in a dataset with 88 patients. The radiomics signatures were significantly associated with NSCLC patients’ survival time. In a Testing dataset, the predicted high risk patients had significantly shorter overall survival than the predicted low risk patients (log-rank $P=$ 0.0004, HR $=$ 2.75, 95% CIs: 1.58–4.80, C-index $=$ 0.64). Further, the novel proposed radiomic nomogram combining the radiomic signature and clinicopathological factors improved the prognostic performance. The CT-based radiomic signature exhibited a good performance for noninvasively identifying patients with NSCLC who should receive postoperative radiation therapy. These results provide a more precise reference for the accurate diagnosis and treatment of NSCLC in clinical.