Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis - a machine learning study.

ABSTRACT:

Objectives

To evaluate the diagnostic performance of multiple machine learning classifier models derived from first-order histogram texture parameters extracted from T1-weighted contrast-enhanced images in differentiating glioblastoma and primary central nervous system lymphoma.

Methods

Retrospective study with 97 glioblastoma and 46 primary central nervous system lymphoma patients. Thirty-six different combinations of classifier models and feature selection techniques were evaluated. Five-fold nested cross-validation was performed. Model performance was assessed for whole tumour and largest single slice using receiver operating characteristic curve.

Results

The cross-validated model performance was relatively similar for the top performing models for both whole tumou

SUBMITTER: Priya S 

PROVIDER: S-EPMC8447821 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature