Paper Info
Title
Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier.
Abstract
The outcome prediction of patients can greatly help to personalize cancer treatment. A large amount of quantitative features (clinical exams, imaging, …) are potentially useful to assess the patient outcome. The challenge is to choose the most predictive subset of features. In this paper, we propose a new feature selection strategy called GARF (genetic algorithm based on random forest) extracted from positron emission tomography (PET) images and clinical data. The most relevant features, predictive of the therapeutic response or which are prognoses of the patient survival 3 years after the end of treatment, were selected using GARF on a cohort of 65 patients with a local advanced oesophageal cancer eligible for chemo-radiation therapy. The most relevant predictive results were obtained with a subset of 9 features leading to a random forest misclassification rate of 18±4% and an areas under the of receiver operating characteristic (ROC) curves (AUC) of 0.823±0.032. The most relevant prognostic results were obtained with 8 features leading to an error rate of 20±7% and an AUC of 0.750±0.108. Both predictive and prognostic results show better performances using GARF than using 4 other studied methods.
Year
DOI
Venue
2017
10.1016/j.compmedimag.2016.12.002
Computerized Medical Imaging and Graphics
Keywords
Field
DocType
Feature selection,Oesophageal cancer,Random forest,Genetic algorithm,Radiomics
Receiver operating characteristic,Feature selection,Pattern recognition,Word error rate,Positron emission tomography,Artificial intelligence,Random forest,Medicine,Cohort,Cancer,Genetic algorithm,Machine learning
Journal
Volume
ISSN
Citations 
60
0895-6111
2
PageRank 
References 
Authors
0.36
13
6
Name
Order
Citations
PageRank
Paul Desbordes130.73
Ruan Su255953.00
Romain Modzelewski320.36
Vauclin Sébastien420.36
Pierre Vera55910.15
Isabelle Gardin6314.55