A functional artificial neural network for noninvasive pretreatment evaluation of glioblastoma patients.

ABSTRACT:

Background

Pretreatment assessments for glioblastoma (GBM) patients, especially elderly or frail patients, are critical for treatment planning. However, genetic profiling with intracranial biopsy carries a significant risk of permanent morbidity. We previously demonstrated that the CUL2 gene, encoding the scaffold cullin2 protein in the cullin2-RING E3 ligase (CRL2), can predict GBM radiosensitivity and prognosis. CUL2 expression levels are closely regulated with its copy number variations (CNVs). This study aims to develop artificial neural networks (ANNs) for pretreatment evaluation of GBM patients with inputs obtainable without intracranial surgical biopsies.

Methods

Public datasets including Ivy-GAP, The Cancer Genome Atlas Glioblastoma (TCGA-GBM), and the

SUBMITTER: Zander E 

PROVIDER: S-EPMC8765794 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature