Abstract BACKGROUND Cognitive impairments caused by paediatric brain tumours negatively impact future quality-of-life. Impairments of processing speed and working memory are important contributors to challenges these children encounter in education. Inconsistent image acquisition and poor imaging quality can restrict advanced radiomic analysis in historical datasets. We developed and refined a language modelling human image-analysis support system, passing data to specific machine learning techniques that can utilise datasets with many data points in a limited population. METHODS Medical records of children treated for brain tumours with surgery and radiotherapy in Manchester between 2000-2020 were reviewed with imaging accessible from 2006-2020. Pre-operative MRI scans were reviewed for 52 patients along with their most recent processing speed and/or working memory test scores. 274 radiological variables analysing tumour features and damage to brain structures were reported per patient using a standardised reporting tool. Univariate analysis was performed using Linear Regression with time-to-test analysed as a covariate. Multivariate analysis used Elastic Net Regression. RESULTS Univariate analysis revealed risk factors for slower processing speed included midline shift, tumour in the corpus callosum, right temporal periventricular white matter oedema, diffusion changes in the left frontal lobe or amygdala and FLAIR changes in the left caudate nucleus (p<0.005). Risk factors for impaired working memory included midline shift, cortical tumour location, left periventricular white matter oedema and diffusion or FLAIR changes in the left frontal lobe (p<0.005). Despite the limited population size and review of only pre-operative features, multivariate analysis produced multiple predictive models able to outperform chance when predicting processing speed and working memory outcomes. CONCLUSIONS This study provides evidence that a radiological language modelling approach can be utilised for outcome prediction. It identified imaging features predictive of cognitive impairments prior to surgery and radiotherapy. Further work is needed to assess inter-observer variability and validate these findings.