A deep learning-based automatic segmentation model for diffuse midline glioma with H3K27M alteration.

ABSTRACT:

Background

Diffuse midline glioma (DMG) is a fatal tumor that emerges in the brainstem and thalamus. Compared with microsurgery and chemotherapy, radiotherapy is currently regarded as a safer and more effective treatment option. However, mapping radiotherapy target on brain stem is extremely demanding. In the study, we build a deep learning-based diffuse midline glioma with H3K27M alteration radiotherapy target area automatic delineation model.

Methods

We collected contrast-enhanced T1-weighted (T1C), T2-weighted, and T2 fluid attenuated inversion recovery (T2-Flair) sequences from patients with DMG and H3K27M alteration from two medical centers to train and test the model. Based on the framework of generative adversarial networks (GANs), we integrated spatial channel attent

SUBMITTER: Deng Y 

PROVIDER: S-EPMC12832457 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature