Utilising functional imaging to predict survival in paediatric brain tumours.

Abstract Introduction Brain tumours are a common cause of death in the paediatric population. We have previously shown that MR imaging and spectroscopy can be used to non-invasively differentiate between tumour types. Here, we demonstrate that functional imaging can be highly predictive of survival and grade in a paediatric cohort. Methods Perfusion (PWI) and diffusion weighted imaging (DWI) were performed in a multi-site (Birmingham Children’s Hospital, Royal Victoria Infirmary, Alder Hey, Nottingham) cohort ([grade, 5-year survival alive:dead number] = [I,15:1],[II, 5:1],[III,2:3],[IV,8:11]). ROIs were drawn on T2 imaging and functional imaging features (mean, standard deviation, skewness, and kurtosis) were derived. Supervised machine learning was used to predict 5-year survival and tumour grade from features. ANOVA and post-hoc tests were used to assess differences in features between grade and 5-year survival status. Results 5-year survival was predicted with 89%, 85%, and 87% accuracy with all imaging, perfusion, or diffusion features, respectively. A significant difference in perfusion was found between surviving and diseased participants (1.71 ± 0.82 vs 2.62 ± 1 mL/100g/min, respectively, p < 0.05). A significant difference in ADC (mm2 s-1) between tumour grades was found (1 vs 4 (1533 ± 458 vs 857 ± 239), 4 vs 3 (857 ± 239 vs 1197 ± 137), 4 vs 2 (857 ± 239 vs 1440 ± 557), corrected p < 0.05). Conclusion We have shown that perfusion and diffusion imaging features can be used to non-invasively assess tumour grade and estimate 5-year survival status in a cohort of paediatric brain tumours.