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

ABSTRACT:

Background

Tumor 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).

Methods

A total of 61pulmonary nodules (PNs) from 51 patients post-operatively diagnosed LUAD were enrolled for analysis. Two datasets were divided according to two-thirds of patients from different commercial Comprehensive Genomic Profiling (CGP) panels: a training cohort including 41 PNs and a testing cohort including rest 20PNs. We sequenced all tumor specimens and paired blood cells using next generation sequencing (NGS),

SUBMITTER: Wang X 

PROVIDER: S-EPMC6775017 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature