Deep learning model outperforms traditional models in clinical data-based prognostic prediction for adult-type diffuse glioma.

ABSTRACT:

Objective

This study aims to systematically compare traditional statistical methods and machine learning approaches for survival prediction in adult-type diffuse glioma (ADG), evaluating their performance, interpretability, and clinical applicability.

Methods

Using two public datasets and one private retrospective cohort, four models (Cox Proportional Hazards, Random Survival Forest, Neural Multi Task Logistic Regression, and DeepSurv) were developed and validated. Missing values were handled in three different ways and subjected to sensitivity analysis.

Results

In both the internal test cohorts and two external validation cohorts, the neural network-based deep learning model outperformed the random survival forest and Cox proportional hazards model, with the DeepSurv

SUBMITTER: Li X 

PROVIDER: S-EPMC12748360 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature