AI-driven breath biopsy from a case-control study assists in the early detection of paediatric brain tumours.

ABSTRACT:

Background

Paediatric brain tumours (PBT) are among the deadliest childhood cancers, with delayed diagnosis often limiting early intervention. This study explores exhaled volatile metabolites as potential biomarkers for PBT, employing AI to identify diagnostic markers and develop a predictive risk assessment model.

Methods

We conducted a case-control study using untargeted volatile metabolomics on exhaled breath samples from 161 patients with primary paediatric brain tumours (PBT) and 140 non-tumour controls, analysed via a custom-built analysis platform. Machine learning, combined with univariate analysis, identified volatile organic compound (VOC) biomarkers linked to PBT. Diagnostic performance was evaluated using four supervised learning classifiers: Support Vector Machi

SUBMITTER: Li S 

PROVIDER: S-EPMC12790591 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature