title: 3D Texture Analysis of Heterogeneous MRI Data for Diagnostic Classification of Childhood Brain Tumours.
abstract: Brain tumours are the most frequently occuring solid tumours affecting childhood, representing 27% of all cancers. The most common posterior fossa tumours are medulloblastoma, pilocytic astrocytoma and ependymoma. Texture Analysis (TA) of Magnetic Resonance Imaging (MRI) aims to represent pixel distributions, intensities and dependencies using mathematically defined features. Such features could potentially provide quantifiable information that is beyond the human vision capabilities, and hence be used to supplement qualitative assessments conducted by radiologists. The primary aim of this study was to carry out a multicentre investigation on the efficacy of 3D TA for diagnostic classification of childhood brain tumours, using conventional MRI images. The data used had been acquired at three different hospitals and consisted of pre-contrast T1 and T2-weighted MRI series, obtained from 121 children diagnosed with medulloblastoma, pilocytic astrocytoma and ependymoma. Using 3D textural features, based on first, second and higher order statistical methods, a support vector machine (SVM) classifier was trained and tested using the leave-one-out cross-validation (LOOCV) approach. An essential outcome of this study is that 3D TA demonstrated a good overall performance, when used on data acquired from a number of centres and using scanners made by different manufacturers and at different magnetic field strengths.
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, Birmingham, UK.
authors.forename: Jan
authors.initials: J
authors.lastname: Novak
authors.affiliation: University of Nottingham, Nottingham, UK.
authors.forename: Daniel
authors.initials: D
authors.lastname: Rodriguez
authors.affiliation: University of Nottingham, Nottingham, UK.
authors.forename: Dorothee P
authors.initials: DP
authors.lastname: Auer
authors.affiliation: University College London, London, UK.
authors.forename: Chris A
authors.initials: CA
authors.lastname: Clark
authors.affiliation: University of Nottingham, Nottingham, UK.
authors.forename: Richard G
authors.initials: RG
authors.lastname: Grundy
authors.affiliation: University Hospital Nottingham, Nottingham, UK.
authors.forename: Tim
authors.initials: T
authors.lastname: Jaspan
authors.affiliation: Birmingham Children's Hospital, 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 / Q000473:pathology*; D002648:Child; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D060388:Support Vector Machine