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The development of a graphical user interface, functional elements and classifiers for the non-invasive characterization of childhood brain tumours using magnetic resonance spectroscopy

Published online by Cambridge University Press:  28 July 2011

Alexander Gibb ,
John Easton ,
Nigel Davies ,
YU Sun ,
Lesley MacPherson ,
Kal Natarajan ,
Theodoros Arvanitis  and
Andrew Peet
Alexander Gibb
Affiliation:
School of Cancer Sciences, University of Birmingham, Birmingham, UK Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
John Easton
Affiliation:
School of Electronic, Electrical and Computer Engineering, University of Birmingham, Birmingham, UK; e-mail: [email protected]
Nigel Davies
Affiliation:
School of Cancer Sciences, University of Birmingham, Birmingham, UK University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
YU Sun
Affiliation:
School of Cancer Sciences, University of Birmingham, Birmingham, UK Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
Lesley MacPherson
Affiliation:
Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
Kal Natarajan
Affiliation:
School of Cancer Sciences, University of Birmingham, Birmingham, UK University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
Theodoros Arvanitis*
Affiliation:
Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK School of Electronic, Electrical and Computer Engineering, University of Birmingham, Birmingham, UK; e-mail: [email protected]
Andrew Peet
Affiliation:
School of Cancer Sciences, University of Birmingham, Birmingham, UK Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
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Abstract

Magnetic resonance spectroscopy (MRS) is a non-invasive method, which can provide diagnostic information on children with brain tumours. The technique has not been widely used in clinical practice, partly because of the difficulty of developing robust classifiers from small patient numbers and the challenge of providing decision support systems (DSSs) acceptable to clinicians. This paper describes a participatory design approach in the development of an interactive clinical user interface, as part of a distributed DSS for the diagnosis and prognosis of brain tumours. In particular, we consider the clinical need and context of developing interactive elements for an interface that facilitates the classification of childhood brain tumours, for diagnostic purposes, as part of the HealthAgents European Union project. Previous MRS-based DSS tools have required little input from the clinician user and a raw spectrum is essentially processed to provide a diagnosis sometimes with an estimate of error. In childhood brain tumour diagnosis where there are small numbers of cases and a large number of potential diagnoses, this approach becomes intractable. The involvement of clinicians directly in the designing of the DSS for brain tumour diagnosis from MRS led to an alternative approach with the creation of a flexible DSS that, allows the clinician to input prior information to create the most relevant differential diagnosis for the DSS. This approach mirrors that which is currently taken by clinicians and removes many sources of potential error. The validity of this strategy was confirmed for a small cohort of children with cerebellar tumours by combining two diagnostic types, pilocytic astrocytomas (11 cases) and ependymomas (four cases) into a class of glial tumours which then had similar numbers to the other diagnostic type, medulloblastomas (18 cases). Principal component analysis followed by linear discriminant analysis on magnetic resonance spectral data gave a classification accuracy of 91% for a three-class classifier and 94% for a two-class classifier using a leave-one-out analysis. This DSS provides a flexible method for the clinician to use MRS for brain tumour diagnosis in children.

Type
Articles
Information
The Knowledge Engineering Review , Volume 26 , Special Issue 3: Computational Intelligence for Neuro-Oncological Diagnosis , 28 July 2011 , pp. 353 - 363
Copyright
Copyright © Cambridge University Press 2011

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