Development and validation of a deep learning survival model for cervical adenocarcinoma patients.

ABSTRACT:

Background

The aim was to develop a personalized survival prediction deep learning model for cervical adenocarcinoma patients and process personalized survival prediction.

Methods

A total of 2501 cervical adenocarcinoma patients from the surveillance, epidemiology and end results database and 220 patients from Qilu hospital were enrolled in this study. We created our deep learning (DL) model to manipulate the data and evaluated its performance against four other competitive models. We tried to demonstrate a new grouping system oriented by survival outcomes and process personalized survival prediction by using our DL model.

Results

The DL model reached 0.878 c-index and 0.09 Brier score in the test set, which was better than the other four models. In the external test

SUBMITTER: Li R 

PROVIDER: S-EPMC10103498 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature