The development of a graphical user interface, functional elements and classifiers for the non-invasive characterization of childhood brain tumours using magnetic resonance spectroscopy
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.
Alexander Gibb, John Easton, Nigel Davies, YU Sun, Lesley MacPherson, Kal Natarajan, Theodoros Arvanitis, Andrew Peet. 2011. The development of a graphical user interface, functional elements and classifiers for the non-invasive characterization of childhood brain tumours using magnetic resonance spectroscopy. The Knowledge Engineering Review. 26:154 doi: 10.1017/S0269888911000154
Alexander Gibb, John Easton, Nigel Davies, YU Sun, Lesley MacPherson, Kal Natarajan, Theodoros Arvanitis, Andrew Peet. 2011. The development of a graphical user interface, functional elements and classifiers for the non-invasive characterization of childhood brain tumours using magnetic resonance spectroscopy. The Knowledge Engineering Review. 26:154 doi: 10.1017/S0269888911000154
The development of a graphical user interface, functional elements and classifiers for the non-invasive characterization of childhood brain tumours using magnetic resonance spectroscopy
Abstract: 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.
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Acknowledgments
The authors thank all of the clinicians who took part in the participatory design approach and in particular the Functional Imaging Group of the UK's Children's Cancer and Leukaemia Group. They also thank members of the Radiology Department at Birmingham Children's Hospital, in particular Shaheen Lateef and Rachel Grazier, for their help in collecting MRS data. The work was funded as part of the HealthAgents project by the European Union IST-2004-27214. AP was part funded by a Department of Health Clinician Scientist Award.
Alexander Gibb, John Easton, Nigel Davies, YU Sun, Lesley MacPherson, Kal Natarajan, Theodoros Arvanitis, Andrew Peet. 2011. The development of a graphical user interface, functional elements and classifiers for the non-invasive characterization of childhood brain tumours using magnetic resonance spectroscopy. The Knowledge Engineering Review. 26:154 doi: 10.1017/S0269888911000154
Alexander Gibb, John Easton, Nigel Davies, YU Sun, Lesley MacPherson, Kal Natarajan, Theodoros Arvanitis, Andrew Peet. 2011. The development of a graphical user interface, functional elements and classifiers for the non-invasive characterization of childhood brain tumours using magnetic resonance spectroscopy. The Knowledge Engineering Review. 26:154 doi: 10.1017/S0269888911000154
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Alexander Gibb, John Easton, Nigel Davies, YU Sun, Lesley MacPherson, Kal Natarajan, Theodoros Arvanitis, Andrew Peet. 2011. The development of a graphical user interface, functional elements and classifiers for the non-invasive characterization of childhood brain tumours using magnetic resonance spectroscopy. The Knowledge Engineering Review. 26:154 doi: 10.1017/S0269888911000154
Alexander Gibb, John Easton, Nigel Davies, YU Sun, Lesley MacPherson, Kal Natarajan, Theodoros Arvanitis, Andrew Peet. 2011. The development of a graphical user interface, functional elements and classifiers for the non-invasive characterization of childhood brain tumours using magnetic resonance spectroscopy. The Knowledge Engineering Review. 26:154 doi: 10.1017/S0269888911000154
