Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme.

ABSTRACT:

Objective

To compare the performance of radiomics-based machine learning survival models in predicting the prognosis of glioblastoma multiforme (GBM) patients.

Methods

131 GBM patients were included in our study. The traditional Cox proportional-hazards (CoxPH) model and four machine learning models (SurvivalTree, Random survival forest (RSF), DeepSurv, DeepHit) were constructed, and the performance of the five models was evaluated using the C-index.

Results

After the screening, 1792 radiomics features were obtained. Seven radiomics features with the strongest relationship with prognosis were obtained following the application of the least absolute shrinkage and selection operator (LASSO) regression. The CoxPH model demonstrated that age (HR = 1.576, p = 0.037)

SUBMITTER: Zhang D 

PROVIDER: S-EPMC10720716 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature