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.

Clinical Decision Support Systems for Brain Tumour Diagnosis and Prognosis: A Systematic Review

“…The HealthAgents project, funded by the European Union, included studies [ 19 , 20 , 21 ]. The HealthAgents network is a globally distributed repository of information and knowledge regarding brain tumour diagnosis and prognosis [ 20 ].…”

“…The HealthAgents network is a globally distributed repository of information and knowledge regarding brain tumour diagnosis and prognosis [ 20 ]. An interactive user interface of HealthAgents was designed by [ 19 ] to facilitate classification of children’s brain tumours. The study was conducted on a cohort of 33 children with cerebellar tumours.…”

“…The various types of CDSSs identified through this review for brain tumour diagnosis and prognosis were Curiam BT [ 16 ], FASMA [ 17 ], MIROR [ 18 ], HealthAgents [ 19 , 20 , 21 ] and INTERPRET [ 22 , 23 , 24 , 25 ]. There have been some significant achievements during the development of such systems worth mentioning.…”

Multicentre evaluation of the INTERPRET decision support system 2.0 for brain tumour classification

“…The INTERPRET approach has also served as a conceptual basis for the design of other DSSs (20)(21)(22) for single-voxel tumour MRS data. A final system called CURIAM (23,24), which includes a 'C 1 versus C 2 versus C 3 ' classifier at short TE and short + long TE, a 'C 1 versus C 2 + C 3 ' and a 'high-grade versus low-grade' classifier for short TE, trained with the INTERPRET data, recently underwent a preliminary evaluation (25) by three radiology residents.…”

Clinical Decision Support Systems for brain tumour diagnosis and prognosis: a systematic review

“…The HealthAgents project, funded by the European Union, included studies [19], [20], and [21]. The HealthAgents network is a globally distributed repository of information and knowledge regarding brain tumour diagnosis and prognosis [20].…”