Radiomic biomarkers for the prediction of immunotherapy outcome in patients with metastatic non-small cell lung cancer.

Abstract

e14520 Background: PD-1 checkpoint inhibitors have recently been approved for the treatment of patients with metastatic NSCLC. Predicting to what extent patients will benefit from these treatments is challenging. Research on predictive biomarkers focus on genetic and histological markers from biopsies, necessarily limited to parts of the tumor. Radiomics is a novel approach to quantify characteristics of the tumor on medical imaging, which may have potential value as non-invasive biomarkers. In this study, we explored the value of radiomic data from primary tumors to predict overall survival (OS) in patients with metastatic NSCLC treated with Nivolumab Methods: We retrospectively selected 64 metastatic NSCLC patients treated in 2ndline setting with Nivolumab (240 mg q 2 weeks until progression). Inclusion criteria were: primary tumor in situ with contrast enhanced CT scan (thorax abdomen; slice thickness ≤3mm) within 6 weeks before the start of the treatment. Exclusion criteria were: presence of atelectasis or fusion with other structures and presence of multiple lung lesions at first scan. A certified radiologist delineated the tumors on CT from which we extracted 1696 Radiomic features. Unsupervised feature selection was performed. Multivariate Cox Regression was used to model the OS based on the selected features, as well as, histopathological type (adeno vs squamous), age at the start of the treatment, and extent of metastatic disease ( < 5, 5–10, or > 10 metastasis) Results: The selection resulted in three textural features derived from a Laplacian of Gaussian (LoG): regions dissimilarity (GLRLM_rlnun), entropy (GLSZM_ze), and uniformity. The resulting model was significant (p = 0.005). Both regions dissimilarity (HR = 0.11, 95% CI 0.03–0.46, p = 0.002) and entropy (HR = 0.20, 95% CI 0.06–0.67, p = 0.009) were significant predictors of OS. Subtype and disease extent were close to significance (p = 0.099) Conclusions: Results indicate that more heterogeneous tumors with irregular patterns of intensities showed better OS. The availability of CT scans makes Radiomics a potentially valuable adjunct to other clinical biomarkers. Further validation of our findings in larger cohorts are warranted