Combining multi-site Magnetic Resonance Imaging with machine learning predicts survival in paediatric brain tumours

• Published in: arXiv.org 2020-04
• Main Authors: Grist, James T, Withey, Stephanie, Bennett, Christopher, Rose, Heather E L, MacPherson, Lesley, Oates, Adam, Powell, Stephen, Novak, Jan, Abernethy, Laurence, Pizer, Barry, Bailey, Simon, Clifford, Steven C, Mitra, Dipayan, Arvanitis, Theodoros N, Auer, Dorothee P, Avula, Shivaram, Grundy, Richard, Peet, Andrew C
• Format: Article
• Language: English
• Subjects: Biomarkers; Brain; Brain cancer; Diagnosis; Machine learning; Magnetic resonance imaging; Medical imaging; Neural networks; Protocol (computers); Survival; Survival analysis; Tumors
• ISSN: 2331-8422

Background Brain tumours represent the highest cause of mortality in the paediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging and spectroscopy. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumour types, especially for rare tumour types such as atypical rhabdoid tumours. Methods 69 children with biopsy-confirmed brain tumours were recruited into this study. All participants had both perfusion and diffusion weighted imaging performed at diagnosis. Data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features, which pertain to survival. Findings Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumours with different survival characteristics (p