Radiomic phenotype features predict pathological response in non-small cell lung cancer.

ABSTRACT:

Background and purpose

Radiomics can quantify tumor phenotype characteristics non-invasively by applying advanced imaging feature algorithms. In this study we assessed if pre-treatment radiomics data are able to predict pathological response after neoadjuvant chemoradiation in patients with locally advanced non-small cell lung cancer (NSCLC).

Materials and methods

127 NSCLC patients were included in this study. Fifteen radiomic features selected based on stability and variance were evaluated for its power to predict pathological response. Predictive power was evaluated using area under the curve (AUC). Conventional imaging features (tumor volume and diameter) were used for comparison.

Results

Seven features were predictive for pathologic gross residual disease (AUC>0.

SUBMITTER: Coroller TP 

PROVIDER: S-EPMC4930885 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature