Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models

ABSTRACT:

Background

Breast cancer (BC) survival prediction can be a helpful tool for identifying important factors selecting the effective treatment reducing mortality rates. This study aims to predict the time-related survival probability of BC patients in different molecular subtypes over 30 years of follow-up.

Materials and methods

This study retrospectively analyzed 3580 patients diagnosed with invasive breast cancer (BC) from 1991 to 2021 in the Cancer Research Center of Shahid Beheshti University of Medical Science. The dataset contained 18 predictor variables and two dependent variables, which referred to the survival status of patients and the time patients survived from diagnosis. Feature importance was performed using the random forest algorithm to identify significant p

SUBMITTER: Zarean Shahraki S 

PROVIDER: S-EPMC10277681 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature