Predicting survival time of lung cancer patients using radiomic analysis.

ABSTRACT:

Objectives

This study investigates the prediction of Non-small cell lung cancer (NSCLC) patient survival outcomes based on radiomic texture and shape features automatically extracted from tumor image data.

Materials and methods

Retrospective analysis involves CT scans of 315 NSCLC patients from The Cancer Imaging Archive (TCIA). A total of 24 image features are computed from labeled tumor volumes of patients within groups defined using NSCLC subtype and TNM staging information. Spearman's rank correlation, Kaplan-Meier estimation and log-rank tests were used to identify features related to long/short NSCLC patient survival groups. Automatic random forest classification was used to predict patient survival group from multivariate feature data. Significance is assessed at P

SUBMITTER: Chaddad A 

PROVIDER: S-EPMC5732814 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature