title: 3D texture analysis of MR images to improve classification of paediatric brain tumours: a preliminary study.
abstract: Brain and central nervous system (CNS) tumours form the second most common group of cancers in children in the UK, accounting for 27% of all childhood cancers. Initial assessment of tumours from MRI scans is usually performed qualitatively, via radiologists' visual inspection. However, different brain tumours do not always demonstrate clear differences in physical appearance, so a diagnosis is usually made via histopathological examination of biopsy samples taken through surgery. This gives rise to the need for accurate, yet non-invasive diagnostic aids. In a previous study, we demonstrated the potential of MRI texture analysis in capturing quantitative information about paediatric brain tumours. In this work, we carry out a preliminary investigation on the use of 3D (volumetric) texture analysis of T1 and T2-weighted MR images in order to classify paediatric brain tumours. We then compare its performance with the traditional 2D texture analysis approach. Our preliminary findings are very encouraging and show that 3D textural features are capable of capturing more discriminative information about the tumours than the traditional 2D approach. However, it remains necessary to expand the work further to include larger cohorts and additional modalities.
authors.affiliation: Institute of Digital Healthcare, WMG, University of Warwick, Coventry, UK.
authors.forename: Ahmed E
authors.initials: AE
authors.lastname: Fetit
authors.affiliation: Birmingham Children's Hospital, NHS Foundation Trust, Birmingham, UK.
authors.forename: Jan
authors.initials: J
authors.lastname: Novak
authors.affiliation: Birmingham Children's Hospital, NHS Foundation Trust, Birmingham, UK.
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Peet
authors.affiliation: Institute of Digital Healthcare, WMG, University of Warwick, Coventry, UK.
authors.forename: Theodoros N
authors.initials: TN
authors.lastname: Arvanitis
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007223:Infant; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity