Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer.

ABSTRACT:

Introduction

"Radiomics" extracts and mines a large number of medical imaging features in a non-invasive and cost-effective way. The underlying assumption of radiomics is that these imaging features quantify phenotypic characteristics of an entire tumor. In order to enhance applicability of radiomics in clinical oncology, highly accurate and reliable machine-learning approaches are required. In this radiomic study, 13 feature selection methods and 11 machine-learning classification methods were evaluated in terms of their performance and stability for predicting overall survival in head and neck cancer patients.

Methods

Two independent head and neck cancer cohorts were investigated. Training cohort HN1 consisted of 101 head and neck cancer patients. Cohort HN2 (n = 95) was u

SUBMITTER: Parmar C 

PROVIDER: S-EPMC4668290 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature