Meta-topologies define distinct anatomical classes of brain tumours linked to histology and survival.

ABSTRACT: The current World Health Organization classification integrates histological and molecular features of brain tumours. The aim of this study was to identify generalizable topological patterns with the potential to add an anatomical dimension to the classification of brain tumours. We applied non-negative matrix factorization as an unsupervised pattern discovery strategy to the fine-grained topographic tumour profiles of 936 patients with neuroepithelial tumours and brain metastases. From the anatomical features alone, this machine learning algorithm enabled the extraction of latent topological tumour patterns, termed meta-topologies. The optimal part-based representation was automatically determined in 10 000 split-half iterations. We further characterized each meta-topology's unique

SUBMITTER: Kernbach JM 

PROVIDER: S-EPMC9830987 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature