Predicting survival in glioblastoma with multimodal neuroimaging and machine learning.

ABSTRACT:

Purpose

Glioblastoma (GBM) is the most common and aggressive malignant glioma, with an overall median survival of less than two years. The ability to predict survival before treatment in GBM patients would lead to improved disease management, clinical trial enrollment, and patient care.

Methods

GBM patients (N = 133, mean age 60.8 years, median survival 14.1 months, 57.9% male) were retrospectively recruited from the neurosurgery brain tumor service at Washington University Medical Center. All patients completed structural neuroimaging and resting state functional MRI (RS-fMRI) before surgery. Demographics, measures of cortical thickness (CT), and resting state functional network connectivity (FC) were used to train a deep neural network to classify patients based on surviva

SUBMITTER: Luckett PH 

PROVIDER: S-EPMC10522528 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature