Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer.

ABSTRACT: No predictive biomarkers can robustly identify patients with non-small cell lung cancer (NSCLC) who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes ("delta") in the radiomic texture (DelRADx) of CT patterns both within and outside tumor nodules before and after two to three cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 patients with NSCLC at two institutions, who were divided into a discovery set (D₁ = 50) and two independent validation sets (D₂ = 62, D₃ = 27). Intranodular and perinodular texture descriptors were extracted, and the relative

SUBMITTER: Khorrami M 

PROVIDER: S-EPMC7718609 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature