Survival outcome prediction in cervical cancer: Cox models vs deep-learning model.

ABSTRACT:

Background

Historically, the Cox proportional hazard regression model has been the mainstay for survival analyses in oncologic research. The Cox proportional hazard regression model generally is used based on an assumption of linear association. However, it is likely that, in reality, there are many clinicopathologic features that exhibit a nonlinear association in biomedicine.

Objective

The purpose of this study was to compare the deep-learning neural network model and the Cox proportional hazard regression model in the prediction of survival in women with cervical cancer.

Study design

This was a retrospective pilot study of consecutive cases of newly diagnosed stage I-IV cervical cancer from 2000-2014. A total of 40 features that included patient demographics, vital

SUBMITTER: Matsuo K 

PROVIDER: S-EPMC7526040 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature