Open AccessEvaluating the Effect of Right-Censored End Point Transformation for Radiomic Feature Selection of Data From Patients With Oropharyngeal Cancer
Author(s) -
Luka Zdilar,
David M. Vock,
G. Elisabeta Marai,
Clifton D. Fuller,
Abdallah S.R. Mohamed,
Hesham Elhalawani,
Baher Elgohari,
Carly Tiras,
Austin Miller,
Guadalupe Canahuate
Publication year - 2018
Publication title -
jco clinical cancer informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.188
H-Index - 12
ISSN - 2473-4276
DOI - 10.1200/cci.18.00052
Subject(s) - random forest , feature selection , statistics , feature (linguistics) , calibration , proportional hazards model , artificial intelligence , statistic , mathematics , computer science , philosophy , linguistics
To evaluate the effect of transforming a right-censored outcome into binary, continuous, and censored-aware representations on radiomics feature selection and subsequent prediction of overall survival (OS) and relapse-free survival (RFS) of patients with oropharyngeal cancer.