Evaluating the Effect of Right-Censored End Point Transformation for Radiomic Feature Selection of Data From Patients With Oropharyngeal Cancer | Zendy

Luka Zdilar | Zendy; David M. Vock | Zendy; G. Elisabeta Marai | Zendy; Clifton D. Fuller | Zendy; Abdallah Mohamed | Zendy; Hesham Elhalawani | Zendy; Baher Elgohari | Zendy; Carly Tiras | Zendy; Austin B. Miller | Zendy; Guadalupe Canahuate | Zendy

Open Access

Evaluating 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 Mohamed,

Hesham Elhalawani,

Baher Elgohari,

Carly Tiras,

Austin B. 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.

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