Assessing Glioblastoma Treatment Response Using Machine Learning Approach Based on Magnetic Resonance Images Radiomics: An Exploratory Study.

ABSTRACT:

Background and objectives

Assessing treatment response in glioblastoma multiforme (GBM) tumors necessitates developing more objective and quantitative approaches. A machine learning-based approach is presented in this exploratory study for GBM patients' treatment response assessment based on radiomics extracted from magnetic resonance (MR) images.

Methods

MR images from 77 GBM patients were acquired at two post-surgery stages and preprocessed. From these images, 107 radiomics were extracted from the segmented tumoral cavities. The most informative features for training machine learning (ML) classifiers were identified using the Spearman correlation analysis of features retained by the forward sequential and LASSO algorithms. Applied machine learning models included support v

SUBMITTER: Sadeghinasab A 

PROVIDER: S-EPMC11683675 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature