CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.

ABSTRACT:

Background and purpose

Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients.

Material and methods

We included two datasets: 98 patients for discovery and 84 for validation. The phenotype of the primary tumor was quantified on pre-treatment CT-scans using 635 radiomic features. Univariate and multivariate analysis was performed to evaluate radiomics performance using the concordance index (CI).

Results

Thirty-five radiomic features were found to be prognostic (CI>0.60, FDR<5%) for DM and twelve for survival. It is noteworthy that tu

SUBMITTER: Coroller TP 

PROVIDER: S-EPMC4400248 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature