Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration.

ABSTRACT:

Background

The prognosis of diffuse midline glioma (DMG) patients with H3K27M (H3K27M-DMG) alterations is poor; however, a model that encourages accurate prediction of prognosis for such lesions on an individual basis remains elusive. We aimed to construct an H3K27M-DMG survival model based on DeepSurv to predict patient prognosis.

Methods

Patients recruited from a single center were used for model training, and patients recruited from another center were used for external validation. Univariate and multivariate Cox regression analyses were used to select features. Four machine learning models were constructed, and the consistency index (C-index) and integrated Brier score (IBS) were calculated. We used the receiver operating characteristic curve (ROC) and area under the rec

SUBMITTER: Huang B 

PROVIDER: S-EPMC10605651 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature