Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features
Abstract
To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans. 553 OPC Patients randomly split into training (80%) and validation (20%), were classified into 2 or 3 risk groups by applying hierarchical clustering over the co-occurrence matrix obtained from a random survival forest (RSF) trained over 301 radiomic features. The cluster...
Paper Details
Title
Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features
Published Date
Jul 7, 2021
Journal
Volume
11
Issue
1
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