Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters.

ABSTRACT:

Purpose/objective

The aim of this study is to predict early distant failure in early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT) using clinical parameters by machine learning algorithms.

Materials/methods

The dataset used in this work includes 81 early stage NSCLC patients with at least 6months of follow-up who underwent SBRT between 2006 and 2012 at a single institution. The clinical parameters (n=18) for each patient include demographic parameters, tumor characteristics, treatment fraction schemes, and pretreatment medications. Three predictive models were constructed based on different machine learning algorithms: (1) artificial neural network (ANN), (2) logistic regression (LR) and (3) support vector machine (SVM). Fur

SUBMITTER: Zhou Z 

PROVIDER: S-EPMC4930894 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature