Classification of High-Grade Glioma into Tumor and Nontumor Components Using Support Vector Machine.

ABSTRACT:

Background and purpose

Current imaging assessment of high-grade brain tumors relies on the Response Assessment in Neuro-Oncology criteria, which measure gross volume of enhancing and nonenhancing lesions from conventional MRI sequences. These assessments may fail to reliably distinguish tumor and nontumor. This study aimed to classify enhancing and nonenhancing lesion areas into tumor-versus-nontumor components.

Materials and methods

A total of 140 MRI scans obtained from 32 patients with high-grade gliomas and 6 patients with brain metastases were included. Classification of lesion areas was performed using a support vector machine classifier trained on 4 components: enhancing and nonenhancing, tumor and nontumor, based on T1-weighted, FLAIR, and dynamic-contrast-enhancing

SUBMITTER: Blumenthal DT 

PROVIDER: S-EPMC7960385 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature