Segmentation-free pretherapeutic assessment of BRAF-status in pediatric low-grade gliomas.

ABSTRACT:

Background

BRAF status is crucial for treating pediatric low-grade gliomas (pLGG) and can be assessed non-invasively from segmented tumor regions on MRI using machine learning (ML). However, there are limitations to manual and automated tumor segmentations. This study assessed the performance of automated segmentation algorithms and a segmentation-free ML classification pipeline.

Methods

Molecularly characterized tumors and whole-brain FLAIR MR images were collected from 455 patients with pLGG treated between 1999 and 2023 at a children's hospital. Three medical segmentation models, TransBTS, MedNeXt, and MedicalNet, were evaluated. Next, we developed a model to identify BRAF status from whole-brain FLAIR MRI, without any reliance on segmentations. We then implemented a nove

SUBMITTER: Kudus K 

PROVIDER: S-EPMC12672580 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature