Decoding tumor mutation burden and driver mutations in early stage lung adenocarcinoma using CT‐based radiomics signature

摘要

BackgroundTumor mutation burden (TMB) is an important determinant and biomarker for response of targeted therapy and prognosis in patients with lung cancer. The present study aimed to determine whether radiomics signature could non‐invasively predict the TMB status and driver mutations in patients with resectable early stage lung adenocarcinoma (LUAD).