Artificial intelligence analysis of temporalis muscle thickness for monitoring sarcopenia and clinical outcomes in individuals with paediatric brain tumours: a retrospective cohort study

BACKGROUND: People with and who have survived paediatric brain tumour (PBT) have a poor quality of life due to physiological frailty, a primary component of which is sarcopenia (ie, low lean muscle mass) and the associated condition, sarcopenic overweight. MRI-based temporalis muscle thickness (TMT) is an image-derived biomarker for lean muscle, frailty, and survival in adult cancers. Here, we evaluated artificial intelligence-based TMT measurements (iTMT) to track sarcopenia in people with PBT at scale and identify trends, risk factors, and associations with morbidity and mortality. METHODS: We conducted the secondary analyses of three cohorts (one prospective trial and two retrospective databases). iTMT was applied to all MRIs from diagnosis until the last follow-up or tumour recurrence to generate longitudinal, patient-level iTMT percentile curves. We investigated the rates of iTMT-defined sarcopenia (iTMT